order is similar to the one in Indonesian. The pronouns have enclitic forms to
mark possessors in a noun phrase or agents in a passive sentence Uchibori and
Shibata (1988).

Sundanese (sun) is a language spoken mainly in the Banten and West Java
provinces. It is the de facto language of provincial identity in western Java.
The main dialects are Bogor (Krawang), Pringan, and Cirebon. It is non-tonal
and has 18 consonant and 7 vowel phonemes. The stress is on the penultimate
syllable. It has elaborate coding of respect levels. It is written in Latin
script since the middle of the 19th century but was previously written in
Arabic, Javanese, and Sundanese scripts. Sundanese is a predominantly SVO
language. It has voice marking and incorporates some (optional) actor-verb
agreement, i.e., number and person Kurniawan (2013).

Tok Pisin (tpi) is an English-based creole and de facto the national language
of Papua New Guinea, a neighboring country of Indonesia. Dialect differences
exist among lowlands, highlands, and islands. Highlands lexicon has more
English influence. It is a non-tonal language and has 16 consonant and 5 vowel
phonemes. It has inclusive/exclusive pronouns and the basic word order is SVO.
It is written in Latin script Eberhard et al. (2021).

Tetun Dili (tdt) is a Tetun-based creole spoken in Dili district, East Timor
north coast as the first language and scattered in western part of East Timor
as the second language. It is a statutory national language according to the
2002 Constitution, Article 13. It has heavy Portuguese (por) and Mambae (mgm)
influence as well as some Indonesian (ind) or Malay influence. It is a non-
tonal language with 22 consonants and 5 vowels. The stress is most commonly on
the penultimate syllable. It has inclusive/exclusive pronouns. The basic word
order is SVO with prepositions and tense-aspect markers. It is a head-initial
language, except for possessors. The speakers of Tetun Dili also use Tetun
[tet], some bilingually, but many others have significant difficulty
understanding it in many domains. It is written in Latin script Eberhard et
al. (2021).

Malayic Dayak (xdy) is a language widely dispersed in Central and West
Kalimantan provinces. It has many dialects and it is written in Latin script
Eberhard et al. (2021). Malayic Dayak is not a proper subgroup, but refers to
the large number of unclassified but clearly Malayic languages of Borneo which
have a three voice system Sommerlot (2020).

##  Appendix B Schemas in NusaCrowd

Schema serves to define and format the attributes of the dataset returned by a
data loader. For each data loader, we implement a source schema, which is
responsible to present the dataset in a format similar to its original
structure, and a nusantara schema, which supports the standardization data
structure across similar tasks.

We define the nusantara schemas as follows. Labels are in string format unless
indicated otherwise.

  * •

Image-text (IMTEXT). This schema could be used for image captioning, text-to-
image generation, and vision-language pre-training. It consists of (id, text,
image_paths, metadata), where id denotes a unique row identifier of the
dataset, text denotes an input text, image_paths denotes a list of paths to
the input image sources, and metadata denotes relevant details such as visual
concepts and labels (if required).

  * •

Speech-text (SPTEXT). This could be used for speech recognition, text-to-
speech (TTS) or speech synthesis, and speech-to-text translation. It consists
of (id, path, audio, text, speaker_id, metadata), where id denotes a unique
row identifier of the dataset, path denotes the file path to an input audio
source, audio denotes the audio data loaded from the corresponding path, text
denotes an input text, speaker_id denotes a unique identifier of the speaker,
metadata denotes relevant details such as the age and gender of the speaker
(if required).

  * •

Speech-to-speech (S2S). This could be used for speech-to-speech translation.
It consists of (id, path_1, audio_1, text_1, metadata_1, path_2, audio_2,
text_2, metadata_2), where id denotes a unique row identifier of the dataset,
path_1 and path_2 denote the file path to a respective input audio source,
audio_1 and audio_2 denote the audio data loaded from the corresponding path,
text_1 and text_2 denote input texts, and metadata_1 and metadata_2 denote
relevant details such as the age of the speaker and their gender (if
required).

  * •

Unlabeled text (SSP). This schema could be used for language modeling in self-
supervised pre-training. It consists of (id, text), where id denotes a unique
row identifier of the dataset and text denotes an input text.

  * •

Single-label text classification (TEXT). This schema could be used for
sentiment analysis, emotion classification, legal classification, and others.
It consists of (id, text, label), where id denotes a unique row identifier of
the dataset, text denotes an input text, and label denotes a deterministic
target variable.

  * •

Multi-label text classification (TEXT MULTI). This schema could be used for
hate speech detection and aspect-based sentiment analysis. It consists of (id,
text, labels), where id denotes a unique row identifier of the dataset, text
denotes an input text, and labels denotes a list of deterministic target
variables.

  * •

Text-to-text (T2T). This schema could be used for machine translation,
summarization, and paraphrasing. It consists of (id, text_1, text_2,
text_1_name, text_2_name), where id denotes a unique row identifier of the
dataset, text_1 and text_2 denote an input text pair, and text_1_name and
text_2_name denote the names of the input text pair (e.g., ind and jav for
translation input text pairs, or document and summary for summarization input
text pairs).

  * •

Sequence labeling (SEQ LABEL). This schema could be used for named entity
recognition (NER), POS tagging, and others. It consists of (id, tokens,
labels), where id denotes a unique row identifier of the dataset, tokens
denotes a list of tokens of an input text, and labels denotes a list of
targets for the tokens.

  * •

Question answering (QA). This schema could be used for extractive QA,
multiple-choice QA, and others. It consists of (id, question_id, document_id,
question, type, choices, context, answer), where id denotes a unique row
identifier of the dataset, question_id denotes a unique identifier of the
question, document_id denotes a unique identifier of the context document,
question denotes an input question to be answered, type denotes the type of
the QA task (e.g., extractive, multiple-choice, open-generative, closed-
generative, etc.), choices denotes a list of answer choices (if required),
context denotes a passage that serves as the background information of the
question (if required), and answer denotes the gold answer to the question (if
required).

  * •

Single-label text pair classification (PAIRS). This could be used for textual
entailment and next sentence prediction. It consists of (id, text_1, text_2,
label), where id denotes a unique row identifier of the dataset, text_1 and
text_2 denote an input text pair, and label denotes the target variable.

  * •

Single-label text pair classification with continuous values or regression
(PAIRS SCORE). This could be used for answer grading and semantic textual
similarity. It consists of (id, text_1, text_2, label), where id denotes a
unique row identifier of the dataset, text_1 and text_2 denote an input text
pair, and label denotes a target variable as a continuous value.

  * •

Multi-label text pair classification (PAIRS MULTI). This could be used for
morphological inflection. It consists of (id, text_1, text_2, labels), where
id denotes a unique row identifier of the dataset, text_1 and text_2 denote an
input text pair, and labels denotes a list of target variables.

  * •

Knowledge base (KB). This schema could be used for constituency parsing,
dependency parsing, coreference resolution, dialogue system, and other tasks
with complex structures. It consists of (id, passages, entities, events,
coreferences, relations). Considering its intricate structure, we encourage
readers to take a look at the implementation of the knowledge base schema.

##  Appendix C Details for Zero-Shot Setting Experiment in NusaNLU

#### Model Checkpoints

For the NLU experiment, we utilize 4 model checkpoints, which are: 1) BLOOMZ
fine-tuned on English prompt with 3B
parameters151515https://huggingface.co/bigscience/bloomz, 2) XGLM with 2.9B
parameters161616https://huggingface.co/facebook/xglm-2.9B, 3) off-the-shelf
XLM-R fine-tuned on XNLI171717https://huggingface.co/joeddav/xlm-roberta-
large-xnli, and 4) XLM-R large fine-tuned on IndoNLI. For XLM-R large fine-
tuned on IndoNLI, we fine-tuned the XLM-R large model with batch size of 128
and initial learning rate of 1e-5 for 50 epochs. We use AdamW optimizer with a
linear learning rate decay and apply early stopping of 5 epochs based on the
validation accuracy score.

#### Prompts

We run the prompting experiment using 3 different prompts for each task type.
We cover several different task types in our NLG experiments, i.e., sentiment
analysis, abusive detection, hate speech detection, emotion classification,
natural language inference (NLI), and next tweet prediction. The prompt
templates used for each task type are shown in Tables A and A.

##  Appendix D Details for Zero-Shot Setting Experiment in NusaNLG

#### Model Checkpoints

For the NLG experiment, we utilize 2 model checkpoints, i.e., BLOOMZ fine-
tuned on English prompt with 3B parameters and XGLM with 2.9B parameters. We
use the same checkpoint as the one used in the zero-shot NLU experiment.

#### Generation Hyperparameters

For generating the prediction sequence, we generate sequence using greedy
decoding with sampling, using top-k of 50 and top-p of 1.0. We force the model
to at least generate one token and limit the generation sequence length to 100
tokens.

#### Prompts

We run the prompting experiment using 3 different prompts for each task type.
We cover two different task types in our NLG experiments, i.e., machine
translation and summarization. The prompt templates used in our NLG experiment
are shown in Table A and Table A.

##  Appendix E Details of Speech Recognition Experiment in NusaASR

#### Model Checkpoints

For both the monolingual and multilingual ASR experiment, we employ 2 wav2vec
2.0LARGE model checkpoints (both with $\sim$300M parameters) as follows: 1)
pre-trained XLSR wav2vec 2.0 model181818wav2vec2-large-
xlsr-53:https://huggingface.co/facebook/wav2vec2-large-xlsr-53 and an off-the-
shelf fine-tuned XLSR wav2vec 2.0 model to Indoensian, Sundanese, and Javanese
speech data191919https://huggingface.co/indonesian-nlp/wav2vec2-indonesian-
javanese-sundanese. For Whisper model we employ the
WhisperSMALL202020https://huggingface.co/openai/whisper-small model with 244M
parameters. For the monolingual experiment, we explore training using the 3
largest and widely-used languages in Indonesia, i.e., Indonesian (ind),
Javanese (jav), and Sundanese (sun).

#### Fine-Tuning Hyperparameters

We apply fine-tuning to both XLSR wav2vec 2.0 and Whisper models for single-
task training, monolingual multi-task training, and multilingual multi-task
training settings. We fine-tune the models using the following
hyperparameters, i.e., Adam optimizer with a learning rate of 5e-5 for the
wav2vec 2.0 model and 1e-4 for the Whisper model, training batch size of 16,
fine-tuning epoch of 30, and apply an early stopping of 5 epoch based on the
validation word error rate (WER). For each model, we search for the best
learning rate ranging from [5e-4 …1e-5]. We run all experiments on a single
A100 GPU.

##  Appendix F Zero-Shot Results of NusaNLU

Here we elaborate further on the analysis in Section 4.1. We report the
overall performances of each model in Figure A and per task performance in
Table A. Predictions derived by prompting BLOOMZ outperform all the other
models and perform on average on par with zero-shot cross-task prompting using
the XLM-R model trained on XNLI. In detail, predictions using cross-task
prompting actually are better in F1 than using BLOOMZ in 17 tasks, while it’s
actually worse in accuracy in 13 tasks, all out of the 26 NLU tasks sampled.
One extreme example can be observed in their performance comparison on the
id_abusive task, where predicting by cross-task prompting XLM-R trained on
XNLI nearly triples the F1 on prompting BLOOMZ. These results suggest that
methods like cross-task prompting are worth exploring, benefitting better
efficiency through cross-task transfer on low-resource language tasks compared
to large multilingual LMs.

Comparing the languages of the prompt, although on both XGLM and BLOOMZ it’s
better to use the English prompt, the difference is actually more apparent on
average when prompting is done using XGLM. However, when we zoom into each of
the tasks, the difference is much larger in prompting using BLOOMZ. The
largest spread is observed on utilizing the English prompt when predicting for
the indolem sentiment analysis task, where the accuracy differs by $\sim$30%,
and the F1 differs by $\sim$37.8%. Comparing the same variables in XGLM, the
largest accuracy difference of $\sim$24% is observed on
id_google_play_review_posneg, and the largest F1 difference of $\sim$19.1% is
observed on Madurese (mad) sentiment analysis task. Furthermore, utilizing
Indonesian prompts is not always the case, worse. On Buginese (bug) sentiment
analysis utilizing BLOOMZ we can get $\sim$23% more accuracy by using
Indonesian prompt. On classifying emotion in emotcmt task utilizing XGLM, we
can get $\sim$7% more F1 by using also the Indonesian prompt. On the indolem
next-tweet-prediction task, utilizing both BLOOMZ and XGLM using also the
Indonesian prompt, we can get additional $\sim$14% accuracy and $\sim$23% F1
respectively.

##  Appendix G Zero-Shot Results of NusaNLG

Here we elaborate further on the analysis in Section 4.2. We report the
overall performances of each model in Figure A and per task performance in
Table A. Generations derived by prompting BLOOMZ are better than prompting
XGLM in all of the tasks except in indosum_fold0_nusantara_t2t, where the
scores differ slightly. The performances in the summarization tasks are
generally lower than the performances in the machine translation tasks. On the
machine translation tasks, the performance in translating to the Indonesian
language as the target language is generally higher than translating to the
local languages, while translating from English to Indonesian is generally
performing the highest.

Prompting using BLOOMZ yields better performances in most of the tasks, when
prompting using English prompts than using Indonesian prompts. In general,
prompting using XGLM yields better generation using Indonesian prompts than
using English prompts. This is especially the case in the machine translation
tasks, where most of them yield better performances except when translating to
Toba Batak (bbc) and Banjarese (bjn) from Indonesian (ind), and also when
translating to Minangkabau (min) to Indonesia (ind) and vice versa. In the
summarization task, prompting using XGLM with English prompts produce better
results than with Indonesian prompts.

It’s worth noting that the translation quality is extremely poor for local
languages, especially in Banjarese (bjn), Acehnese (ace), Toba Batak (bbc),
Ngaju (nij), Madurese (mad), and Sundanese (sun). This is even more severe
when those local languages become the target languages. This finding suggests
that both BLOOMZ and XGLM still fail to learn the representation of these
local languages.

##  Appendix H ASR Results of NusaASR

Here we elaborate further on the analysis in Section 4.3. We report the per-
task performance of each model in Table A for the wav2vec 2.0-pt model, Table
A for the wav2vec 2.0 model-ft, and Table A for the Whisper model. The best
overall performance is achieved by wav2vec 2.0-pt fine-tuned in multilingual
multi-task setting, achieving 17.03% average WER over all tasks. The model
also performs better in most cases for languages other than Indonesian
compared to the wav2vec 2.0-ft model. While for Indonesian, wav2vec 2.0-ft
fine-tuned in all 3 training settings, i.e., multilingual multi-task,
monolingual (ind) multi-task, and single-task settings, achieve much better
scores, i.e., $<$5% WER over all Indonesian tasks.

Comparing the performance per language, the best Indonesian ASR model achieves
very low WER on Indonesian (ind) speech corpora, i.e., indspeech_digit_cdsr,
indspeech_news_lvcsr, indspeech_teldialog_lvcsr, and
indspeech_teldialog_svcsr. Compared to local languages, i.e., Minangkabau
(min), Sundanese (sun), Javanese (jav), Balinese (ban), Acehnese (ace), Batak
(btk), and Buginese (bug), the performance of the best ASR model only achieves
$\sim$10-30% WER. The performance is especially low for Buginese (bug), which
suggests distinct speech features are required for handling speech recognition
in Buginese (bug) language. This fact aligns with the result of prior work in
Indonesian local languages Winata et al. (2023), where Buginese (bug) has
inferior performance in the leave-one-language-out setting.

##  Appendix I Private Datasets in NusaCrowd

NusaCrowd offers access to 14 previously private datasets. We provide the
details of all 14 previously private datasets listed in NusaCrowd along with
the task, languages, and modality in Table A.

##  Appendix J Comparison with Other Initiatives

To provide a broader perspective of the impact of NusaCrowd, we provide the
comparison of NusaCrowd initiatives with other global, regional, and
Indonesian data initiatives in Table A.

##  Appendix K Details of Datasets in NusaCrowd

LABEL:tab:stats-nusacrowd provides the details description, license,
languages, dataset volume, annotation quality, and other metadata of all 137
datasets collected in NusaCrowd.

Language |  Prompt in Sentiment Analysis Task
---|---
Indonesian (ind) |  [INPUT]\nApakah sentimen dari teks tersebut? [LABELS_CHOICE]
Apakah sentimen dari teks berikut?\nTeks: [INPUT]\nSentimen: [LABELS_CHOICE]
Teks: [INPUT]\n\nTolong prediksikan sentimen dari teks diatas: [LABELS_CHOICE]
English (eng) |  [INPUT]\nWhat would be the sentiment of the text above? [LABELS_CHOICE]
What is the sentiment of this text?\nText: [INPUT]\nSentiment: [LABELS_CHOICE]
Text: [INPUT]\n\nPlease classify the sentiment of above text: [LABELS_CHOICE]
Table A: Prompt used for Sentiment Analysis task Language |  Prompt in Emotion Classification Task
---|---
Indonesian (ind) |  [INPUT]\nApakah emosi dari teks diatas? [LABELS_CHOICE]
Apakah emosi dari teks ini?\n Teks: [INPUT]\n Emosi: [LABELS_CHOICE]
Teks: [INPUT]\n\nTolong prediksikan emosi dari teks diatas: [LABELS_CHOICE]
English (eng) |  [INPUT]\nWhat would be the emotion of the text above? [LABELS_CHOICE]
What is the emotion of this text?\nText: [INPUT]\nEmotion: [LABELS_CHOICE]
Text: [INPUT]\n\nPlease classify the emotion of above text: [LABELS_CHOICE]
Table A: Prompt used for Emotion Classification task Language |  Prompt in Abusive Detection Task
---|---
Indonesian (ind) |  [INPUT]\nApakah teks diatas kasar? [LABELS_CHOICE]
Apakah teks berikut ini kasar?\n[INPUT]\nJawab dengan [OPTIONS]:
[LABELS_CHOICE]
[INPUT]\nApakah menurutmu teks diatas itu [OPTIONS]? [LABELS_CHOICE]
English (eng) |  [INPUT]\nIs the text abusive? [LABELS_CHOICE]
Is the following text abusive?\n[INPUT]\nAnswer with [OPTIONS]:
[LABELS_CHOICE]
[INPUT]\nDo you think the text is [OPTIONS]? [LABELS_CHOICE]
Table A: Prompt used for Abusive Detection task Language |  Prompt in Clickbait Detection Task
---|---
Indonesian (ind) |  [INPUT]\nApakah judul diatas clickbait? [LABELS_CHOICE]
Apakah judul berikut ini clickbait?\n[INPUT]\nJawab dengan [OPTIONS]:
[LABELS_CHOICE]
[INPUT]\nApakah menurutmu teks diatas itu [OPTIONS]? [LABELS_CHOICE]
English (eng) |  [INPUT]\nIs the title clickbait? [LABELS_CHOICE]
Is the following title a clickbait?\n[INPUT]\nAnswer with [OPTIONS]:
[LABELS_CHOICE]
[INPUT]\nDo you think the text is [OPTIONS]? [LABELS_CHOICE]
Table A: Prompt used for Clickbait Detection task Language |  Prompt in Rating Review Regression Task
---|---
Indonesian (ind) |  [INPUT]\nBerapa rating dari teks review tersebut, dari 1 sampai 5? [LABELS_CHOICE]
[INPUT]\nDari 1 sampai 5, berapa rating dari review diatas? [LABELS_CHOICE]
[INPUT]\nDari 1 sampai 5 bintang, bagaimana menurutmu rating dari review
tersebut? [LABELS_CHOICE]
English (eng) |  [INPUT]\nWhat is the rating of the review above, from 1 to 5? [LABELS_CHOICE]
[INPUT]\nFrom 1 to 5, what is the rating of the review above? [LABELS_CHOICE]
[INPUT]\nFrom 1 to 5 stars, how would you rate the previous review?
[LABELS_CHOICE]
Table A: Prompt used for Rating Review Regression task Language |  Prompt in Hate Speech Detection Task
---|---
Indonesian (ind) |  [INPUT]\nApakah teks diatas hatespeech? [LABELS_CHOICE]
Apakah teks berikut ini hatespeech\n[INPUT]\nJawab dengan [OPTIONS]:
[LABELS_CHOICE]
[INPUT]\nApakah menurutmu teks diatas itu [OPTIONS]? [LABELS_CHOICE]
English (eng) |  [INPUT]\nDo you think the text is hatespeech? Answer: [LABELS_CHOICE]
Is the following text a hatespeech?\n[INPUT]\nAnswer with [OPTIONS]:
[LABELS_CHOICE]
[INPUT]\nDo you think the text is [OPTIONS]? [LABELS_CHOICE]
Table A: Prompt used for Hate Speech Detection task Language |  Prompt in Next Tweet Prediction Task
---|---
Indonesian (ind) |  Diberikan dua tweet\nA: [INPUT_A]\nB: [INPUT_B]\n\nApakah tweet B adalah sambungan dari tweet A? [LABELS_CHOICE]
Apakah tweet "[INPUT_B]" adalah sambungan dari tweet "[INPUT_A]"?
[LABELS_CHOICE]
Tweet pertama: [INPUT_A].\nApakah "[INPUT_B]" merupakan sambungan dari tweet
pertama? [LABELS_CHOICE]
English (eng) |  Given two tweets\nA: [INPUT_A]\nB: [INPUT_B]\n\nIs tweet B is a continuation of tweet A? [LABELS_CHOICE]
Is tweet "[INPUT_B]" a continuation of tweet "[INPUT_A]"? [LABELS_CHOICE]
First Tweet: [INPUT_A].\nWould "[INPUT_B]" a continuation of the first tweet?
[LABELS_CHOICE]
Table A: Prompt used for Next Tweet Prediction task Language |  Prompt in NLI Task
---|---
Indonesian (ind) |  [INPUT_A]\nBerdasarkan kutipan sebelumnya, apakah benar bahwa "[INPUT_B]"? [OPTIONS]? [LABELS_CHOICE]
[INPUT_A]\n\nPertanyaan: Apakah kalimat tersebut mengimplikasikan bahwa
"[INPUT_B]"? [OPTIONS]? [LABELS_CHOICE]
Diberikan [INPUT_A]. Apakah kalimat tersebut sesuai dengan [INPUT_B]?
[OPTIONS]? [LABELS_CHOICE]
English (eng) |  [INPUT_A]\nBased on the previous passage, is it true that "[INPUT_B]"? Yes, no, or maybe? [LABELS_CHOICE]
[INPUT_A]\n\nQuestion: Does this imply that "[INPUT_B]"? Yes, no, or maybe?
[LABELS_CHOICE]
Given that [INPUT_A]. Does it follow that [INPUT_B]? Yes, no, or maybe?
[LABELS_CHOICE]
Table A: Prompt used for Natural Language Inference task Language |  Prompt in Summary Task
---|---
Indonesian (ind) |  [INPUT]\n===\nTulis rangkuman dari teks diatas dalam bahasa Indonesia:
Artikel dalam bahasa Indonesia: [INPUT]\nRangkuman dalam bahasa Indonesia:
[SOURCE]\nBagaimana kamu merangkum teks diatas dalam bahasa Indonesia?
English (eng) |  [INPUT]\n===\nWrite a summary of the text above in Indonesian:
Article in Indonesian: [INPUT]\nSummary in Indonesian:
[SOURCE]\nHow would you rephrase that briefly in Indonesian?
Table A: Prompt used for Summary task Language |  Prompt in Translation Task
---|---
Indonesian (ind) |  Terjemahkan teks berikut dari bahasa [SOURCE] ke bahasa [TARGET].\nTeks: [INPUT]\nTerjemahan:
[INPUT]\nTerjemahkan teks diatas dari bahasa [SOURCE] ke bahasa [TARGET].
Teks dalah bahasa [SOURCE]: [INPUT]\nBagaimana kamu menterjemahkan teks diatas
dalam bahasa [TARGET]?
English (eng) |  Translate the following text from [SOURCE] to [TARGET].\nText: [INPUT]\nTranslation:
[INPUT]\nTranslate the text above from [SOURCE] to [TARGET].
Text in [SOURCE]: [INPUT]\nHow would you translate that in [TARGET]?
Table A: Prompt used for Translation task

Figure A: Zero-shot generalization to NLU tasks in NusaNLU. Box plots show
summary statistics on accuracy (left) and F1 (right). Points are per-dataset
scores from the average of performances using three different prompts.

Dataset Name | Lang | Task | XLM-R XNLI | XLM-R IndoNLI | XGLM 2.9B (id) | XGLM 2.9B (en) | BLOOMZ 3B (id) | BLOOMZ 3B (en)
---|---|---|---|---|---|---|---|---
acc | f1 | acc | f1 | acc | f1 | acc | f1 | acc | f1 | acc | f1
code_mixed_jv_id_id | ind | SA | 29.79 | 29.66 | 16.48 | 15.20 | 71.00 | 29.10 | 15.59 | 14.90 | 21.73 | 22.88 | 19.17 | 23.96
code_mixed_jv_id_jv | jav | SA | 32.86 | 34.12 | 17.71 | 18.42 | 70.83 | 28.65 | 13.92 | 12.27 | 18.22 | 18.38 | 16.07 | 16.58
emot | ind | EMOT | 49.77 | 47.95 | 33.64 | 29.14 | 30.68 | 20.82 | 29.39 | 19.18 | 45.61 | 38.66 | 43.18 | 35.29
emotcmt | ind | EMOT | 43.81 | 41.48 | 28.87 | 25.24 | 32.25 | 25.88 | 26.12 | 19.00 | 45.48 | 33.38 | 56.41 | 44.68
emotion_id_opinion | ind | EMOT | 50.83 | 49.37 | 12.98 | 11.65 | 29.25 | 21.90 | 31.46 | 30.48 | 48.15 | 45.79 | 49.90 | 49.98
id_abusive | ind | AD | 49.01 | 35.11 | 42.91 | 32.97 | 40.38 | 19.72 | 77.24 | 34.08 | 77.54 | 33.11 | 55.87 | 28.92
id_abusive_news_comment | ind | AD | 30.78 | 21.74 | 7.29 | 8.29 | 25.20 | 23.16 | 22.26 | 20.11 | 28.37 | 22.21 | 31.04 | 25.51
id_google_play_review | ind | RR | 9.59 | 12.94 | 51.22 | 23.70 | 42.38 | 14.82 | 43.70 | 16.70 | 73.90 | 37.64 | 63.70 | 35.97
id_google_play_review_posneg | ind | SA | 92.32 | 85.63 | 89.78 | 76.38 | 83.84 | 52.25 | 83.25 | 65.93 | 93.44 | 87.15 | 87.76 | 81.28
id_hatespeech | ind | HSD | 77.70 | 75.31 | 59.47 | 59.39 | 40.21 | 34.32 | 36.19 | 32.76 | 62.60 | 40.44 | 64.56 | 42.23
id_hoax_news | ind | HD | 48.40 | 46.51 | 45.60 | 45.54 | 37.87 | 30.23 | 39.33 | 34.64 | 53.07 | 38.20 | 53.07 | 44.96
id_hsd_nofaaulia | ind | HSD | 72.53 | 51.40 | 67.03 | 51.94 | 30.04 | 28.18 | 30.77 | 29.30 | 63.74 | 41.40 | 76.19 | 51.75
id_short_answer_grading | ind | SAG | 17.50 | 7.56 | 17.75 | 5.03 | 12.58 | 4.67 | 21.29 | 8.55 | 20.41 | 8.70 | 19.24 | 5.45
id_stance | ind | SD | 10.68 | 8.12 | 24.04 | 16.04 | 48.96 | 26.64 | 63.20 | 25.82 | 31.55 | 14.41 | 58.46 | 29.33
imdb_jv | jav | SA | 21.01 | 14.45 | 33.22 | 20.71 | 49.47 | 38.74 | 48.77 | 39.44 | 37.42 | 31.25 | 31.27 | 31.13
indo_law | ind | LG | 53.64 | 53.51 | 53.90 | 53.40 | 52.58 | 34.52 | 52.59 | 34.46 | 52.64 | 43.29 | 47.38 | 35.67
indolem_ntp | ind | NTP | 61.26 | 32.95 | 31.69 | 20.72 | 33.87 | 32.56 | 43.20 | 40.03 | 76.21 | 57.08 | 77.60 | 53.95
indolem_sentiment | ind | SA | 70.82 | 69.91 | 55.49 | 55.48 | 69.83 | 56.86 | 59.05 | 55.10 | 81.80 | 77.19 | 82.33 | 79.15
indonli | ind | NLI | 35.77 | 28.02 | 35.52 | 27.84 | 35.82 | 29.71 | 35.59 | 24.36 | 52.27 | 41.38 | 56.82 | 44.62
indotacos | ind | LG | 5.13 | 3.81 | 16.15 | 9.01 | 4.93 | 3.87 | 4.51 | 1.51 | 12.00 | 7.52 | 5.47 | 2.92
jadi_ide | jav | DI | 41.61 | 33.73 | 30.12 | 28.18 | 33.33 | 21.06 | 33.33 | 22.70 | 33.40 | 18.31 | 33.75 | 17.99
karonese_sentiment | btx | SA | 41.30 | 39.82 | 27.50 | 16.96 | 34.20 | 17.04 | 37.07 | 21.63 | 35.30 | 25.70 | 40.70 | 21.44
nusax_senti_ace | ace | SA | 53.50 | 44.74 | 39.50 | 22.25 | 38.42 | 19.52 | 44.92 | 33.71 | 60.92 | 46.59 | 60.92 | 46.17
nusax_senti_ban | ban | SA | 54.50 | 44.20 | 45.50 | 31.61 | 39.33 | 21.23 | 50.17 | 37.30 | 60.08 | 45.75 | 61.42 | 46.50
nusax_senti_bjn | bjn | SA | 61.50 | 53.97 | 47.00 | 33.34 | 38.33 | 18.97 | 38.67 | 26.98 | 49.67 | 36.42 | 52.42 | 39.56
nusax_senti_bug | bug | SA | 44.00 | 40.43 | 37.50 | 18.58 | 39.58 | 22.15 | 49.33 | 35.79 | 65.58 | 50.09 | 67.33 | 51.14
nusax_senti_eng | eng | SA | 71.75 | 61.48 | 55.75 | 43.33 | 38.08 | 18.95 | 38.25 | 24.91 | 46.67 | 33.78 | 49.33 | 37.04
nusax_senti_ind | ind | SA | 70.50 | 59.28 | 59.25 | 46.83 | 54.75 | 41.21 | 58.25 | 43.79 | 73.17 | 55.51 | 73.33 | 55.70
nusax_senti_jav | jav | SA | 64.75 | 55.11 | 54.25 | 41.69 | 45.00 | 29.42 | 61.42 | 46.05 | 73.25 | 55.54 | 73.75 | 55.93
nusax_senti_mad | mad | SA | 60.25 | 51.24 | 44.00 | 29.23 | 40.67 | 22.70 | 52.58 | 39.47 | 66.08 | 50.50 | 66.00 | 50.11
nusax_senti_min | min | SA | 62.00 | 53.36 | 49.00 | 36.17 | 38.50 | 19.46 | 47.67 | 35.69 | 58.00 | 44.17 | 60.50 | 46.09
nusax_senti_nij | nij | SA | 54.75 | 47.52 | 42.00 | 26.59 | 38.67 | 20.25 | 52.42 | 39.39 | 63.17 | 48.18 | 65.00 | 49.39
nusax_senti_sun | sun | SA | 63.25 | 53.50 | 49.50 | 37.25 | 38.67 | 20.12 | 43.50 | 31.58 | 57.17 | 43.36 | 59.75 | 45.34
nusax_senti_bbc | bbc | SA | 46.50 | 37.67 | 39.75 | 23.11 | 39.25 | 20.85 | 49.83 | 37.25 | 57.50 | 43.77 | 58.58 | 44.50
sentiment_nathasa_review | ind | SA | 25.41 | 21.71 | 14.00 | 12.12 | 20.33 | 12.44 | 16.67 | 8.12 | 29.93 | 24.23 | 30.76 | 27.69
smsa | ind | SA | 80.20 | 64.69 | 71.40 | 53.24 | 55.33 | 37.28 | 69.33 | 50.92 | 79.87 | 58.38 | 80.00 | 58.50
su_emot | sun | SA | 45.95 | 44.35 | 29.59 | 27.72 | 28.49 | 16.25 | 25.06 | 11.16 | 34.59 | 25.25 | 24.99 | 10.16
wrete | ind | ENT | 44 | 30.56 | 4.00 | 3.85 | 47.33 | 29.97 | 59.33 | 31.59 | 11.67 | 9.98 | 30.67 | 23.46

Table A: Details of zero-shot generalization to NLU tasks in NusaNLU. EMOT
denotes emotion classification, AD denotes abusive detection, RR denotes
review rating, SA denotes sentiment analysis, HD denotes hoax detection, HSD
denotes hate speech detection, SAG denotes short answer grading, SF denotes
stance detection, LG denotes legal classification, NLI denotes natural
language inference, NTP denotes next tweet prediction, and ENT denotes
entailment.

Figure A: Zero-shot generalization to NLG tasks in NusaNLG. Box plots show
summary statistics on SacreBLEU (left) and ROUGE-L (right). Points are per-
dataset scores from the average of performances using 3 different prompts.

Dataset Name | Lang | Task | XGLM 2.9B (id) | XGLM 2.9B (en) | BLOOMZ 3B (id) | BLOOMZ 3B (en)
---|---|---|---|---|---|---
SacreBLEU | ROUGEL | SacreBLEU | ROUGEL | SacreBLEU | ROUGEL | SacreBLEU | ROUGEL
bible_en_id | eng-ind | MT | 1.11 | 8.41 | 0.49 | 3.69 | 3.39 | 20.52 | 3.82 | 22.81
bible_jv_id | jav-ind | MT | 0.18 | 5.13 | 0.10 | 1.16 | 0.47 | 7.23 | 0.43 | 8.02
bible_su_id | sun-ind | MT | 0.18 | 4.95 | 0.05 | 1.20 | 0.44 | 7.11 | 0.64 | 7.81
id_panl_bppt | eng-ind | MT | 2.58 | 10.56 | 0.48 | 2.77 | 6.36 | 26.43 | 7.81 | 28.98
indo_general_mt_en_id | eng-ind | MT | 3.51 | 9.41 | 1.35 | 3.40 | 8.82 | 25.51 | 9.92 | 26.82
indo_religious_mt_en_id | eng-ind | MT | 1.80 | 9.27 | 0.25 | 1.81 | 4.49 | 22.06 | 5.94 | 26.76
minangnlp_mt | min-ind | MT | 2.12 | 7.14 | 2.25 | 7.19 | 5.15 | 17.81 | 7.34 | 26.68
news_en_id | eng-ind | MT | 2.66 | 10.26 | 0.48 | 2.62 | 6.56 | 24.56 | 8.83 | 28.92
nusax_mt_ace_ind | ace-ind | MT | 0.72 | 6.55 | 0.47 | 3.19 | 1.47 | 11.47 | 1.77 | 13.28
nusax_mt_ban_ind | ban-ind | MT | 1.09 | 6.94 | 0.82 | 3.68 | 2.58 | 12.92 | 2.54 | 14.61
nusax_mt_bbc_ind | bbc-ind | MT | 1.59 | 7.94 | 0.89 | 4.33 | 3.03 | 15.49 | 3.96 | 18.59
nusax_mt_bjn_ind | bjn-ind | MT | 0.30 | 4.43 | 0.28 | 2.06 | 0.52 | 6.80 | 0.58 | 7.66
nusax_mt_bug_ind | bug-ind | MT | 2.06 | 10.31 | 0.19 | 2.50 | 3.12 | 22.78 | 4.33 | 24.55
nusax_mt_eng_ind | eng-ind | MT | 0.72 | 4.77 | 0.66 | 3.38 | 1.00 | 6.84 | 1.76 | 10.35
nusax_mt_ind_ace | ind-ace | MT | 1.03 | 5.26 | 0.77 | 4.30 | 1.72 | 8.34 | 3.43 | 15.45
nusax_mt_jav_ind | jav-ind | MT | 0.53 | 5.47 | 0.30 | 2.48 | 1.13 | 10.00 | 1.38 | 10.71
nusax_mt_mad_ind | mad-ind | MT | 1.81 | 8.64 | 1.06 | 4.83 | 3.39 | 16.11 | 5.28 | 20.66
nusax_mt_min_ind | min-ind | MT | 0.64 | 6.14 | 0.63 | 3.38 | 1.39 | 11.40 | 1.68 | 12.07
nusax_mt_nij_ind | nij-ind | MT | 0.84 | 6.73 | 0.81 | 4.08 | 2.01 | 12.80 | 2.26 | 13.54
nusax_mt_sun_ind | sun-ind | MT | 0.39 | 5.00 | 0.29 | 2.40 | 0.98 | 8.94 | 1.18 | 9.96
nusax_mt_ind_ban | ind-ban | MT | 1.67 | 7.19 | 1.10 | 4.88 | 1.83 | 10.43 | 3.66 | 15.10
nusax_mt_ind_bbc | ind-bbc | MT | 0.33 | 2.46 | 0.36 | 2.64 | 0.47 | 4.05 | 0.67 | 4.69
nusax_mt_ind_bjn | ind-bjn | MT | 0.57 | 3.70 | 2.15 | 12.76 | 3.16 | 19.82 | 4.66 | 24.47
nusax_mt_ind_bug | ind-buh | MT | 0.86 | 4.75 | 0.70 | 3.73 | 1.39 | 8.92 | 2.28 | 12.59
nusax_mt_ind_eng | ind-buh | MT | 0.44 | 3.50 | 0.42 | 3.00 | 0.77 | 5.73 | 0.86 | 6.09
nusax_mt_ind_jav | ind-jav | MT | 1.87 | 7.69 | 0.85 | 4.30 | 2.47 | 10.62 | 6.01 | 21.41
nusax_mt_ind_mad | ind-mad | MT | 0.71 | 4.69 | 0.59 | 3.31 | 0.72 | 6.98 | 1.36 | 8.91
nusax_mt_ind_min | ind-min | MT | 0.99 | 5.41 | 0.76 | 3.80 | 2.00 | 10.50 | 4.01 | 15.56
nusax_mt_ind_nij | ind-nij | MT | 0.50 | 3.39 | 0.47 | 3.30 | 0.71 | 4.83 | 1.43 | 7.87
nusax_mt_ind_sun | ind-sun | MT | 1.14 | 7.27 | 0.59 | 3.74 | 2.11 | 13.00 | 2.06 | 13.87
parallel_su_id | sun-ind | MT | 0.91 | 6.70 | 0.89 | 3.84 | 3.88 | 13.71 | 4.68 | 14.95
ted_en_id | eng-ind | MT | 2.68 | 10.99 | 0.49 | 2.44 | 7.94 | 28.55 | 8.90 | 31.34
ud_id_csui | ind-eng | MT | 1.06 | 3.69 | 5.58 | 16.68 | 8.54 | 27.28 | 14.94 | 38.89
indosum_fold0 | ind | SUM | 2.64 | 5.53 | 9.73 | 12.24 | 3.55 | 12.21 | 3.07 | 11.88
xl_sum | ind | SUM | 0.35 | 4.05 | 0.33 | 3.95 | 2.66 | 12.68 | 3.30 | 14.31
liputan6 | ind | SUM | 1.47 | 4.12 | 1.92 | 7.48 | 3.62 | 13.92 | 2.67 | 13.13
|  |  | EM | F1 | EM | F1 | EM | F1 | EM | F1
facqa | ind | QA | 0.21 | 5.61 | 0.21 | 4.72 | 61.95∗ | 81.97∗ | 61.95∗ | 82.02∗
idk_mrc | ind | QA | 0.71 | 3.67 | 0.28 | 4.31 | 37.05∗ | 45.51∗ | 34.91∗ | 43.57∗
tydiqa_ind | ind | QA | 0.31 | 5.52 | 0.27 | 4.67 | 63.08∗ | 77.28∗ | 62.85∗ | 77.09∗

Table A: Details of zero-shot generalization to NLG tasks in NusaNLG. MT
denotes machine translation, SUM denotes summarization, and QA denotes
question answering. * the test data might be (partially) seen during the
language pre-training and prompt tuning phase.

Dataset | Lang | Multilingual | Monolingual Multi-task | Single-task
---|---|---|---|---
Multi-task | ind | jav | sun
indspeech_digit_cdsr | ind | 2.36 | 0.22 | 97.51 | 97.65 | 43.84
indspeech_news_lvcsr | ind | 13.04 | 3.31 | 84.88 | 81.39 | 1.41
indspeech_teldialog_lvcsr | ind | 1.59 | 0.37 | 81.16 | 77.65 | 0.45
indspeech_teldialog_svcsr | ind | 9.01 | 1.72 | 94.58 | 97.03 | 0.65
librivox_indonesia_ind | ind | 14.24 | 15.41 | 86.23 | >100 | 16.20
librivox_indonesia_ace | ace | 40.85 | 95.14 | >100 | 92.36 | 100
indspeech_newstra_ethnicsr_ban | ban | 12.21 | >100 | 56 | 59.14 | 43.98
librivox_indonesia_ban | ban | 21.24 | >100 | 78.04 | >100 | 100
indspeech_newstra_ethnicsr_btk | btk | 18.98 | >100 | 81.24 | 74.67 | 64.77
librivox_indonesia_bug | bug | 41.59 | 96.70 | >100 | >100 | 100
indspeech_news_ethnicsr_jv | jav | 13.37 | >100 | 37.50 | 93.32 | 100
indspeech_newstra_ethnicsr_jav | jav | 13.30 | >100 | 42.70 | 72.77 | 57.34
librivox_indonesia_jav | jav | 29.05 | 81.18 | 60.70 | >100 | 100
librivox_indonesia_min | min | 16.94 | 46.19 | 68.10 | 98.57 | 100
indspeech_news_ethnicsr_su | sun | 20.54 | 84.08 | 84.17 | 50.17 | 100
indspeech_newstra_ethnicsr_sun | sun | 12.41 | >100 | 65.40 | 47.38 | 49.03
librivox_indonesia_sun | sun | 8.85 | 60.69 | 60.12 | 23.70 | 100
Average | 17.03 | >100 | 75.98 | 82.37 | 63.39

Table A: Per task WER (lower is better) of the wav2vec 2.0-pt model on all 17
ASR tasks in NusaASR.

Dataset | Lang | Multilingual | Monolingual Multi-task | Single-task
---|---|---|---|---
Multi-task | ind | jav | sun
indspeech_digit_cdsr | ind | 0.38 | 0.18 | 92.86 | 97.46 | 0.22
indspeech_news_lvcsr | ind | 0.82 | 0.65 | 67.33 | 80.12 | 1.37
indspeech_teldialog_lvcsr | ind | 0.59 | 0.09 | 68.65 | 74.26 | 0.22
indspeech_teldialog_svcsr | ind | 0.41 | 0.33 | 92.92 | 94.89 | 0.46
librivox_indonesia_ind | ind | 7.32 | 8.11 | 72.57 | >100 | 8.37
librivox_indonesia_ace | ace | 31.94 | 91.67 | 90.28 | 89.58 | 49.31
indspeech_newstra_ethnicsr_ban | ban | 22.95 | >100 | 36.57 | 43.69 | 21.50
librivox_indonesia_ban | ban | 19.16 | >100 | 68.69 | >100 | 35.98
indspeech_newstra_ethnicsr_btk | btk | 35.99 | >100 | 59.79 | 61.34 | 40.92
librivox_indonesia_bug | bug | 53.30 | >100 | >100 | >100 | 90.09
indspeech_news_ethnicsr_jv | jav | 22.30 | >100 | 17.90 | 96.73 | 27.13
indspeech_newstra_ethnicsr_jav | jav | 21.41 | >100 | 22.10 | 50.14 | 25.10
librivox_indonesia_jav | jav | 38.93 | 96.49 | 41.70 | >100 | 44.10
librivox_indonesia_min | min | 18.10 | 70.48 | 52.86 | 79.05 | 24.29
indspeech_news_ethnicsr_su | sun | 35.47 | 87.02 | 74.13 | 43.25 | 44.38
indspeech_newstra_ethnicsr_sun | sun | 19.39 | >100 | 39.67 | 25.71 | 20.45
librivox_indonesia_sun | sun | 7.51 | 67.63 | 49.13 | 6.36 | 15.03
Average | 19.76 | >100 | 61.76 | 75.76 | 26.41

Table A: Per task WER (lower is better) of the wav2vec 2.0-ft modes on all 17
ASR tasks in NusaASR.

Dataset | Lang | Multilingual | Monolingual Multi-task | Single-task
---|---|---|---|---
Multi-task | ind | jav | sun
indspeech_digit_cdsr | ind | 0.27 | 0.30 | 43.61 | 79.08 | 3.94
indspeech_news_lvcsr | ind | 2.56 | 2.40 | 59.95 | 97.42 | 2.08
indspeech_teldialog_lvcsr | ind | 0.38 | 0.57 | 58.14 | 94.44 | 0.97
indspeech_teldialog_svcsr | ind | 50.85 | 50.91 | 72.47 | 87.69 | 100
librivox_indonesia_ind | ind | 87.43 | 17.32 | 88.15 | 99.70 | 10.13
librivox_indonesia_ace | ace | 97.92 | 99.31 | 98.61 | >100 | 37.50
indspeech_newstra_ethnicsr_ban | ban | 51.89 | 51.81 | 57.85 | 95.56 | 70.66
librivox_indonesia_ban | ban | 88.32 | >100 | 87.38 | 99.07 | 39.25
indspeech_newstra_ethnicsr_btk | btk | 63.85 | 56.52 | 82.16 | 98.39 | >100
librivox_indonesia_bug | bug | 100 | >100 | 99.06 | >100 | 56.60
indspeech_news_ethnicsr_jv | jav | 58.24 | 96.31 | 30.82 | >100 | 53.84
indspeech_newstra_ethnicsr_jav | jav | 47.55 | 49.45 | 42.77 | 95.99 | 73.60
librivox_indonesia_jav | jav | 85.71 | >100 | 46.94 | >100 | 51.84
librivox_indonesia_min | min | 80.95 | 82.38 | 78.57 | 90.95 | 36.67
indspeech_news_ethnicsr_su | sun | 64.18 | 97.28 | 78.37 | 68.91 | 67.02
indspeech_newstra_ethnicsr_sun | sun | 54.65 | 53.51 | 66.26 | 73.02 | 72.71
librivox_indonesia_sun | sun | 77.46 | 88.44 | 68.21 | 41.62 | 27.17
Average | 59.54 | 62.83 | 68.20 | 89.98 | 49.44

Table A: Per task WER (lower is better) of the Whisper model on all 17 ASR tasks in NusaASR. Dataset | Task | Modal |  Languages
---|---|---|---
Korpus Nusantara Indrayana (2016); Hasbiansyah et al. (2016); Ningtyas et al. (2018); Etsa et al. (2018); Darwis et al. (2019); Wahyuni et al. (2019); Sujaini (2019, 2020); Gunawan et al. (2021) | MT | Text |  ind, jav, xdy, bug, sun, mad, bjn, bbc, hak, msa, min, nan
Karonese dataset Karo et al. (2022) | SA | Text |  btx
Sundanese-Indonesian Parallel Corpus Ardiyanti Suryani et al. (2022b) | MT | Text |  sun, ind
PoSTagged Sundanese Monolingual Corpus Ardiyanti Suryani et al. (2022a) | POS | Text |  sun
Code-mixed Sentiment JV-ID Tho et al. (2021) | SA | Text |  ind
ID-HSD-Nofaaulia Aulia and Budi (2019) | HSD | Text |  ind
Indo Wiki Paralel Corpora Trisedya and Inastra (2014) | MT | Text |  ind, jav, sun, min
INDspeech_DIGIT_CDSR Sakti et al. (2004) | ASR | Speech |  ind
INDspeech_NEWS_EthnicSR Sani et al. (2012) | ASR | Speech |  sun, jav
INDspeech_NEWS_LVCSR Sakti et al. (2008a, 2004, 2013) | ASR | Speech |  ind
INDspeech_NEWS_TTS Sakti et al. (2008b, 2010, 2013) | TTS | Speech |  ind
INDspeech_NEWSTRA_EthnicSR Sakti and Nakamura (2013, 2014); Novitasari et al. (2020) | ASR | Speech |  sun, jav, btk, ban
INDspeech_TELDIALOG_LVCSR Sakti et al. (2008a, 2004, 2013) | ASR | Speech |  ind
INDspeech_TELDIALOG_SVCSR Sakti et al. (2004) | ASR | Speech |  ind
Table A: List of private datasets that have been made public through NusaCrowd
initiative. MT denotes machine translation, SA denotes sentiment analysis, POS
denotes POS tagging, HSD denotes hate speech detection, ASR denotes automatic
speech recognition, and TTS denotes text-to-speech.

Project | #Dataset | Languages | Modality | Open/crowd | Opening access
---|---|---|---|---|---
initiative? | to private data
Global Data Initiative
XTREME Hu et al. (2020) | 9 | 40 | text | ✗ | ✗
XGLUE Liang et al. (2020) | 11 | 19 | text | ✗ | ✗
GEM Gehrmann et al. (2021) | 11 | 18 | text | ✓ | ✗
GEMv2 Gehrmann et al. (2022) | 40 | 51 | text | ✓ | ✗
Other Regional Data Initiative
CLUE Xu et al. (2020) | 9 | 1 (zho) | text | ✗ | ✗
KLUE Park et al. (2021) | 8 | 1 (kor) | text | ✗ | ✗
ALUE Seelawi et al. (2021) | 9 | 1 (ara) | text | ✗ | ✗
IndicGLUE Kakwani et al. (2020) | 14 | 12 Indian languages | text | ✗ | ✗
IndicNLG Kumar et al. (2022) | 5 | 11 Indian languages | text | ✗ | ✗
IndicXTREME Doddapaneni et al. (2022) | 103 | 18 Indian languages | text | ✗ | ✗
Indonesian Data Initiative
IndoNLU Wilie et al. (2020) | 12 | 1 (ind) | text | ✗ | ✗
IndoLEM Koto et al. (2020b) | 12 | 1 (ind) | text | ✗ | ✗
IndoNLG Cahyawijaya et al. (2021b) | 10 | 3 (ind, sun, jav) | text | ✗ | ✗
NusaCrowd | 137 | 19 Indonesian languages | text, speech, image | ✓ | ✓

Table A: Comparison of NusaCrowd with other similar initiatives.

Table A: Overview of all datasets in NusaCrowd. Dataset |  Description |  License |  Year |  Language |  Annotation Quality |  Data Volume |  Data Unit |  Split
---|---|---|---|---|---|---|---|---
AM2iCo Liu et al. (2021b) |  We present AM2ICO (Adversarial and Multilingual Meaning in Context), a wide coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts for 14 language pairs. |  CC-BY 4.0 |  2021 |  ind, eng |  Crawling & human annotation |  3,098 |  examples |  1589 train, 500 validation, 1000 test
Barasa |  Barasa: Indonesian SentiWordNet for sentiment analysis |  MIT |  2015 |  ind |  Unknown |  16 |  MB |  No dataset split
CASA Ilmania et al. (2018) |  CASA is an aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms. The dataset covers six aspects of car quality, where each label represents a sentiment for a single aspect with three possible values: positive, negative, and neutral. |  CC-BY-SA 4.0 |  2018 |  ind |  Crawling & human annotation |  1,080 |  sentences |  810 train, 90 validation, 180 test
CC100 Conneau et al. (2020) |  CC100 comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository. |  Common Crawl’s license |  2020 |  ind, sun, jav |  Machine generated / Crawling w/o curation |  36,052 |  MB |  No dataset split
COCO Captions ID Sinurat (2019) |  COCO Captions contains over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions are be provided for each image. This is an Indonesian version of COCO translated using Google Translate. |  CC-BY 4.0 |  2019 |  ind |  Machine generated / Crawling w/o curation |  123,287 |  sentences |  113287 train, 5000 validation, 5000 test
CORD Park et al. (2019) |  In this paper, we introduce a novel dataset called CORD, which stands for a Consolidated Receipt Dataset for post-OCR parsing. To the best of our knowledge, this is the first publicly available dataset which includes both box-level text and parsing class annotations. The parsing class labels are provided in two-levels. The eight superclasses include store, payment, menu, subtotal, and total. The eight superclasses are subdivided into 54 subclasses, e.g., store has nine subclasses including name, address, telephone, and fax. Furthermore, it also provides line annotations for the serialization task which is a newly emerging problem as a combination of the two tasks. |  CC-BY 4.0 |  2019 |  ind |  Crawling & human annotation |  1,000 |  receipts |  800 train, 100 validation, 100 test
CVSS Jia et al. (2022) |  We introduce CVSS, a massively multilingual-to-English speech-to-speech translation (S2ST) corpus, covering sentence-level parallel S2ST pairs from 21 languages into English. CVSS is derived from the Common Voice (Ardila et al., 2020) speech corpus and the CoVoST 2 (Wang et al., 2021b) speech-to-text translation (ST) corpus, by synthesizing the translation text from CoVoST 2 into speech using state-of-the-art TTS systems. Two versions of translation speech in English are provided: 1) CVSS-C: All the translation speech is in a single high-quality canonical voice; 2) CVSS-T: The translation speech is in voices transferred from the corresponding source speech. In addition, CVSS provides normalized translation text which matches the pronunciation in the translation speech. |  CC-BY 4.0 |  2022 |  ind |  Crawling & human annotation |  6 |  hours |  2.6 train, 1.8 validation, 1.9 test
Cendana Moeljadi et al. (2019) |  Cendana is a linguistically annotated corpus that includes some grammatical analyses, such as parts-of-speech, phrases, relations between entities, and meaning representations. Cendana is built using tools developed in the Deep Linguistic Processing with HPSG (DELPHIN) community. |  GNU General Public License, version 2 |  2019 |  ind |  Human generation & curation |  552 |  sentences |  No dataset split
CoVoST 2 Wang et al. (2021) |  With the aim to foster research in massive multilingual ST and ST for low resource language pairs, we release CoVoST 2, a large-scale multilingual ST corpus covering translations from 21 languages into English and from English into 15 languages. This represents the largest open dataset available to date from total volume and language coverage perspective. |  CC0 |  2020 |  ind, eng |  Crawling & human annotation |  3 |  hours |  1 train, 1 validation, 1 test
Code-mixed Sentiment JV-ID Tho et al. (2021) |  Dataset terdiri dari 3.963 kalimat code-mixing dalam bahasa Indonesia dan bahasa Jawa yang dikumpulkan dari twitter. Label dataset terdiri dari 3 kelas sentimen, yaitu: positif, negatif, dan netral. Label sentimen dikumpulkan dengan melakukan anotasi manual untuk setiap tweet. |  CC-BY 3.0 |  2021 |  ind |  Human generation & curation |  977 |  tweets |  No dataset split
Cross-lingual Outline- based Dialogue (COD) Majewska et al. (2023) |  Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding, dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages: Arabic, Indonesian, Russian, and Kiswahili. The data covers multi domain instances, e.g., bank, travel, weather, movies, music. |  Unknown |  2022 |  ind |  Machine generated w/ human curation |  194 |  dialogues |  0 train, 92 validation, 102 test
Customer Review (Natasha Skincare) Nurlaila et al. (2017) |  This dataset is collected from tweet costumer review from Natasha Skincare. This dataset contain of label emotion (joy, sad, angry, fear, disgust, surprise, or no emotion). |  Unknown |  2017 |  ind |  Crawling & human annotation |  124,263 |  tweets |  87120 train, 37143 validation, 37143 test
EmoT (IndoNLU Split) Saputri et al. (2018) |  EmoT is an emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, appiness, love, and sadness. |  CC-BY-SA 4.0 |  2018 |  ind |  Human generation & curation |  4,403 |  sentence pairs |  3521 train, 440 validation, 442 test
EmotCMT Yulianti et al. (2021) |  EmotCMT is a Indonesian-English code-switching data collected from Twitter for emotion classification task. |  Unknown |  2021 |  ind, eng |  Crawling & Human annotation |  582 |  sentences |  No dataset split
Emotion Indonesian Public Opinion Riccosan et al. (2022) |  The dataset is formed from Indonesian tweet containing six emotion values, namely anger, fear, joy, love, sad, and neutral. The total data in this dataset is 7,080 and it is fully cleaned and fully annotated. Each label has a varied amount of data distribution, including 1,130 data for anger, 911 data for fear, 1,275 data for joy, 760 data for love, 1,003 data for sad, and 2,001 data for neutral. |  CC-BY-NC-ND 4.0 |  2022 |  ind |  Human generation & curation |  7,080 |  tweets |  No dataset split
FacQA Purwarianti et al. (2007) |  The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article . Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the corresponding short passage. There are six categories of questions: date, location, name, organization, person, and quantitative. |  CC-BY-SA 4.0 |  2007 |  ind |  Human generation & curation |  3,117 |  documents |  2495 train, 311 dev, 311 test
HoASA (IndoNLU Split) Azhar et al. (2019) |  HoASA is an aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, AiryRooms |  CC-BY-SA 4.0 |  2019 |  ind |  Crawling & human annotation |  9,450 |  sentences |  7,560 train, 1890 test
Human Instructions - Indonesian (wikihow) Chocron and Pareti (2018) |  Human Instructions - Indonesian (wikihow) is 39.246 Human Instructions in Indonesian Extracted from wikiHow. Step-by-step instructions in Indonesian extracted from wikiHow and decomposed into a formal graph representation in RDF. Instructions are represented in RDF following the PROHOW vocabulary and data model. For example, the category, steps, requirements and methods of each set of instructions have been extracted. This dataset has been produced as part of the The Web of Know-How project. |  CC-BY-NC-SA 4.0 |  2017 |  ind |  Crawling & human annotation |  39,246 |  documents |  No dataset split
ID Abusive Ibrohim and Budi (2018) |  ID Abusive is a Twitter dataset for abusive language detection in Indonesian. Pre-defined abusive words are used as queries to collect the tweets. The dataset is labeled into 3 labels: not abusive language, abusive but not offensive, and offensive language by 20 volunteer annotators. |  CC-BY-NC-SA 4.0 |  2018 |  ind |  Crawling & human annotation |  2,016 |  tweets |  No dataset split
ID Abusive Online News Comment Kiasati Desrul and Romadhony (2019) |  The dataset consists of comments that are in some of the top news stories in 2019, obtained from several online news/forum, such as: kompas, kaskus, and detik. The labeling process is carried out by a total of 10 annotators and each comment is annotated by 3 annotators. Each comment was labeled with one of the following labels: not abusive, abusive but not offensive, abusive and offensive. |  Unknown |  2019 |  ind |  Machine generated / Crawling w/o curation |  3,184 |  comments |  No dataset split
ID Coreference Resolution Suherik and Purwarianti (2017) |  ID Coreference resolution is news dataset aimed for coreference resolution task. This dataset consists of 1030 manually labelled sentences derived from IDENTIC parallel corpus. |  Unknown |  2017 |  ind |  Crawling & human annotation |  1,030 |  sentences |  759 train, 0 validation, 108 test
ID Multilabel HS Ibrohim and Budi (2019) |  ID Multilabel HS consists of hate speech and abusive language Twitter dataset from several previous researches consisting of (Alfina et al., 2017, 2018), (Putri, 2018), and (Ibrohim and Budi, 2018), and new tweets queried using specific abusive words/phrases. Labels used in the dataset are: HS (hate speech label), Abusive (abusive language label), HS_Individual (hate speech targeted to an individual), HS_Group (hate speech targeted to a group), HS_Religion (hate speech related to religion/creed), HS_Race (hate speech related to race/ethnicity), HS_Physical (hate speech related to physical/disability), HS_Gender (hate speech related to gender/sexual orientation), HS_Physical (hate related to other invective/slander), HS_Weak (weak hate speech), HS_Moderate (moderate hate speech), HS_Strong (strong hate speech). |  CC-BY-NC-SA 4.0 |  2019 |  ind |  Crawling & human annotation |  13,169 |  tweets |  No dataset split
ID Quora Question Pairs |  Quora Question Pairs (QQP) dataset consists of over 400,000 question pairs, and each question pair is annotated with a binary value indicating whether the two questions are paraphrase of each other. This dataset is translated version of QQP to Indonesian Language. |  Custom212121https://www.quora.com/about/tos |  2021 |  ind |  Machine generated / Crawling w/o curation |  149,011 |  sentence pairs |  134084 train, 14927 validation, 0 test
ID Short Answer Grading Haidir and Purwarianti (2020) |  ID Short Answer Grading dataset is dataset of questions using Edukasystem platform. It used 4 exams consisting of Biology and Geography subject. Two exams are used for training data and 2 exams are used for testing data. Exam used for training data has 15 questions and exam for testing data has 3 questions. The dataset has 30 questions + 7605 short answers as training data and 6 questions + 1560 short answers as testing data. The number of respondents is 534 different respondents. Assessment was carried out by 2 experts for Biology subject and 5 experts for Geography subject. The assessment is carried out on a scale of 0 to 5. |  Unknown |  2020 |  ind |  Human generation & curation |  9,165 |  sentences |  7605 train, 0 validation, 1560 test
ID-HSD-Nofaaulia Aulia and Budi (2019) |  There have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the possibility of the message of the text may be hidden. In this research, we explore in detecting hate speech on Indonesian long documents using machine learning approach. We build a new Indonesian hate speech dataset from Facebook. |  Unknown |  2022 |  ind |  Crawling & human annotation |  906 |  documents |  815 train, 0 validation, 91 test
ID-HSD-Riomulia Alfina et al. (2017a) |  ID-HSD-RioMulia composed of tweets about the Jakarta Governor Election 2017, whose selection of candidates potentially triggers hate speech in relation to race, religion, and gender. Each tweet is labelled as either containing hate speech or not by 30 human annotators. |  GNU General Public License v3.0 |  2017 |  ind |  Crawling & human annotation |  713 |  tweets |  No dataset split
IDK-MRC Putri and Oh (2022) |  IDK-MRC is an Indonesian Machine Reading Comprehension dataset that covers answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA, the new unanswerable question in IDK-MRC is generated using question generation model and human-written question. Each paragraph in the dataset have a set of answerable and unanswerable question with the corresponding answer. (Note: the paper for this dataset is still under review on EMNLP 2022 – the anonymity period ends on Oct 6, 2022) |  CC-BY-SA 4.0 |  2022 |  ind |  Machine generated w/ human curation |  10,940 |  paragraph, question, and answer pairs |  9332 train, 764 validation, 844 test
IMDb Javanese Wongso et al. (2021) |  Large Movie Review Dataset translated to Javanese. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. |  Unknown |  2021 |  jav |  Machine generated / Crawling w/o curation |  50,000 |  sentences |  25000 train, 0 validation, 25000 test
INDspeech_DIGIT_CDSR Sakti et al. (2004) |  INDspeech_DIGIT_CDSR is the first Indonesian speech dataset for connected digit speech recognition (CDSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan and Bandung Institute of Technology (ITB) under the Asia-Pacific Telecommunity (APT) project in 2004 [Sakti et al., 2004]. Although it was originally developed for a telecommunication system for hearing and speaking-impaired people, it can be used for other applications, i.e., automatic call centers that recognize telephone numbers. |  CC-BY-NC-SA 4.0 |  2004 |  ind |  Human generation & curation |  12444 [214] |  utterances [speakers] |  8440 train, 0 validation, 4004 test
INDspeech_NEWSTRA_EthnicSR Sakti and Nakamura (2013) |  INDspeech_NEWSTRA_EthnicSR is a collection of graphemically balanced and parallel speech corpora of four major Indonesian ethnic languages: Javanese, Sundanese, Balinese, and Bataks. It was developed in 2013 by the Nara Institute of Science and Technology (NAIST, Japan) [Sakti et al., 2013]. The data has been used to develop Indonesian ethnic speech recognition in supervised learning [Sakti et al., 2014] and semi-supervised learning [Novitasari et al., 2020] based on the Machine Speech Chain framework [Tjandra et al., 2020]. |  CC-BY-NC-SA 4.0 |  2013 |  ind, sun, jav, ban, btk |  Human generation & curation |  13000 [40] |  utterances [speakers] |  9000 train, 0 validation, 4000 test
INDspeech_NEWS_EthnicSR Sani et al. (2012) |  INDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora (Javanese and Sundanese) for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Science and Technology (NAIST, Japan) in collaboration with the Bandung Institute of Technology (ITB, Indonesia) [Sani et al., 2012]. Furthermore, as all speakers utter the same sentences, it can also be used for voice conversion tasks. |  CC-BY-NC-SA 4.0 |  2012 |  sun, jav |  Human generation & curation |  2300 [20] |  utterances [speakers] |  2000 train, 0 validation, 300 test
INDspeech_NEWS_LVCSR Sakti et al. (2008a) |  INDspeech_NEWS_LVCSR is the first Indonesian speech dataset for large vocabulary continuous speech recognition (LVCSR) with more than 40 hours of speech and 400 speakers [Sakti et al., 2008]. R&D Division of PT Telekomunikasi Indonesia (TELKOMRisTI) developed the data in 2005-2006, in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan, as the continuation of the Asia-Pacific Telecommunity (APT) project [Sakti et al., 2004]. It has also been successfully used for developing Indonesian LVCSR in the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013]. |  CC-BY-NC-SA 4.0 |  2008 |  ind |  Human generation & curation |  44000 [400] |  utterances [speakers] |  39600 train, 0 validation, 4400 test
INDspeech_NEWS_TTS Sakti et al. (2008b) |  INDspeech_NEWS_TTS is a speech dataset for developing an Indonesian text-to-speech synthesis system [Sakti et al., 2008, Sakti et al., 2010]. The data was developed by Advanced Telecommunication Research Institute International (ATR) Japan under the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013]. |  CC-BY-NC-SA 4.0 |  2008 |  ind |  Human generation & curation |  2,012 |  utterances |  1972 train, 0 validation, 40 test
INDspeech_TELDIALOG_LVCSR Sakti et al. (2008a) |  INDspeech_TELDIALOG_LVCSR is one of the first Indonesian speech datasets for large vocabulary continuous speech recognition (LVCSR) [Sakti et al., 2008]. R&D Division of PT Telekomunikasi Indonesia (TELKOMRisTI) developed the data in 2005-2006, in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan, as the continuation of the Asia-Pacific Telecommunity (APT) project [Sakti et al., 2004]. It has also been successfully used for developing Indonesian LVCSR in the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013]. |  CC-BY-NC-SA 4.0 |  2008 |  ind |  Human generation & curation |  40000 [400] |  utterances [speakers] |  36000 train, 0 validation, 4000 test
INDspeech_TELDIALOG_SVCSR Sakti et al. (2004) |  INDspeech_TELDIALOG_SVCSR is the first Indonesian speech dataset for small vocabulary continuous speech recognition (SVCSR). The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan and Bandung Institute of Technology (ITB) under the Asia-Pacific Telecommunity (APT) project in 2004 [Sakti et al., 2004]. Although it was originally developed for a telecommunication system for hearing and speaking impaired people, it can be used for other applications, i.e., automatic call centers. Furthermore, as all speakers utter the same sentences, it can also be used for voice conversion tasks. |  CC-BY-NC-SA 4.0 |  2004 |  ind |  Human generation & curation |  20000 [200] |  utterances [speakers] |  10000 train, 0 validation, 10000 test
Identic (Larasati, 2012) Larasati (2012) |  IDENTIC is an Indonesian-English parallel corpus for research purposes. The corpus is a bilingual corpus paired with English. The aim of this work is to build and provide researchers a proper Indonesian-English textual dataset and also to promote research in this language pair. The corpus contains texts coming from different sources with different genres. |  Unknown |  2012 |  eng, ind |  Crawling & human annotation |  45,000 |  sentences |  No dataset split
Idn-tagged-corpus-CSUI Dinakaramani et al. (2014) |  Id-tagged-corpus-CSUI is a POS tagging dataset contains about 10,000 sentences, collected from the PAN Localization Project tagged with 23 POS tag classes. |  CC-BY-SA 4.0 |  2014 |  ind |  Crawling & human annotation |  10,000 |  sentences |  8000 train, 1000 validation, 1029 test
InSet Lexicon Koto and Rahmaningtyas (2017) |  Sentiment analysis from Twitter |  Unknown |  2017 |  ind |  Crawling & human annotation |  2,630 |  sentences |  2630 test
IndQNER |  IndQNER is a NER dataset created by manually annotating 8 chapters in the Indonesian translation of Quran text. The dataset consists of 2476 named entities from 18 categories. Each named entity is labeled using BIO (Beginning-Inside-Outside) tagging format. |  Unknown |  2022 |  ind |  Human generation & curation |  3,118 |  sentences |  2494 train, 312 validation, 312 test
Indo4B Wilie et al. (2020) |  Indo4B is an Indonesian pre-training corpus collected from multiple online sources, Indo4B consists of 3.6B tokens and over more than 250M sentences. Indo4B has been used to pre-trained a large pre-trained language model called IndoBERT and IndoBERT-lite. |  CC-BY-NC-SA 4.0 |  2020 |  ind |  Machine generated / Crawling w/o curation |  3.6B |  tokens |  No dataset split
Indo4B Plus Cahyawijaya et al. (2021b) |  Indo4BPlus is an Indonesian pre-training corpus derived from Indo4B. Indo4BPlus covers three languages in Indonesia, i.e., Indonesian, Javanese, and Sundanese. Indo4BPlus consists of 4B tokens with over more than 300M documents. |  CC-BY-NC-SA 4.0 |  2021 |  ind, sun, jav |  Machine generated / Crawling w/o curation |  4B |  tokens |  No dataset split
IndoAMR Ilmy and Khodra (2020) |  IndoAMR is annotated Indonesia AMR parser from Indonesian simple sentences. |  Unknown |  2021 |  ind |  Crawling & human annotation |  1,130 |  sentences |  700 train, 0 validation, 300 test
IndoCollex Wibowo et al. (2021) |  List of Colloquial word Transformation with its label. e.g.: makan -> mkn (shortening). Data is published on IndoCollex: A Testbed for Morphological Transformation of Indonesian Word Colloquialism Research Paper published on ACL-IJCNLP 2021. Useful for morphological research. |  MIT |  2021 |  ind |  Human generation & curation |  2,126 |  sentence pairs |  1637 train, 182 validation, 193 test
IndoCoref Artari et al. (2021) |  IndoCoref is a coreference resolution dataset collected from Wikipedia. IndoCoref consists of 201 passages from wikipedia with manually labelled coreference by five annotators. |  MIT |  2021 |  ind |  Human generation & curation |  201 |  documents |  No dataset split
IndoLEM NTP Koto et al. (2020b) |  IndoLEM next tweet prediction is a next tweet prediction dataset collected from tweeter |  CC BY-SA 3.0 |  2020 |  ind |  Crawling & human annotation |  8,382 |  instances |  5681 train, 811 validation, 1890 test
IndoLEM Sentiment Analysis Koto et al. (2020b) |  IndoLEM Sentiment Analysis is a textual sentiment analysis dataset collected from twitter |  CC BY-SA 3.0 |  2020 |  ind |  Crawling & human annotation |  5,048 |  sentences |  3638 train, 399 validation, 1011 test
IndoLEM Tweet Ordering Koto et al. (2020b) |  IndoLEM tweet ordering is a text ordering dataset collected from tweeter |  CC BY-SA 3.0 |  2020 |  ind |  Crawling & human annotation |  7,608 |  instances |  5327 train, 760 validation, 1521 test
IndoNLG Bible En-Id Cahyawijaya et al. (2021b) |  Bible En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the bible. We also add a Bible dataset to the English Indonesian translation task. Specifically, we collect an Indonesian and an English language Bible and generate a verse-aligned parallel corpus for the English-Indonesian machine translation task. We split the dataset and use 75% as the training set, 10% as the validation set, and 15% as the test set. Each of the datasets is evaluated in both directions, i.e., English to Indonesian (En $\rightarrow$ Id) and Indonesian to English (Id $\rightarrow$ En) translations. |  CC-BY-SA 4.0 |  2021 |  eng, ind |  Crawling & human annotation |  31,078 |  sentences |  23308 training, 3109 validation, 4661 test
IndoNLG Bible Jv-Id Cahyawijaya et al. (2021b) |  Bible Jv-Id is a machine translation dataset containing Indonesian-Javanese parallel sentences collected from the bible. Analogous to the En $\leftrightarrow$ Id and Su $\leftrightarrow$ Id datasets, we create a new dataset for Javanese and Indonesian translation generated from the verse-aligned Bible parallel corpus with the same split setting. In terms of size, both the Su $\leftrightarrow$ Id and Jv $\leftrightarrow$ Id datasets are much smaller compared to the En $\leftrightarrow$ Id dataset, because there are Bible chapters for which translations are available for Indonesian, albeit not for the local languages. |  CC-BY-SA 4.0 |  2021 |  jav, ind |  Crawling & human annotation |  7,957 |  sentences |  5967 train, 797 validation, 1193 test
IndoNLG Bible Su-Id Cahyawijaya et al. (2021b) |  Bible Su-Id is a machine translation dataset containing Sundanese Indonesian parallel sentences collected from the bible. Analogous to the En $\leftrightarrow$ Id and Su $\leftrightarrow$ Id datasets, we create a new dataset for Javanese and Indonesian translation generated from the verse-aligned Bible parallel corpus with the same split setting. In terms of size, both the Su $\leftrightarrow$ Id and Jv $\leftrightarrow$ Id datasets are much smaller compared to the En $\leftrightarrow$ Id dataset, because there are Bible chapters for which translations are available for Indonesian, albeit not for the local languages |  CC-BY-SA 4.0 |  2021 |  sun, ind |  Crawling & human annotation |  7,958 |  sentences |  5968 train, 797 validation, 1193 test
IndoNLI Mahendra et al. (2021) |  IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian. IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. |  CC-BY-SA 4.0 |  2021 |  ind |  Human generation & curation |  17,712 |  sentence pairs |  10330 train, 2197 validation, 5183 test
IndoNLU NERGrit Wilie et al. (2020) |  NER Grit dataset is a NER dataset taken from the Grit-ID repository, and the labels are spans in IOB chunking representation. The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and ORGANIZATION (name of organization). |  CC-BY-SA 4.0 |  2020 |  ind |  Unknown |  2,090 |  sentences |  1672 train, 209 validation, 209 test
IndoPuisi |  Puisi is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi (poem) with its title and author. The data was scraped online using BeautifulSoup. The title and author column was produced using regex. |  MIT |  2020 |  ind |  Machine generated / Crawling w/o curation |  7,223 |  documents |  No dataset split
IndoSum Kurniawan and Louvan (2018) |  The Indosum dataset was collected from news aggregators covering six topics: entertainment, inspiration, sport, showbiz, headline, and technology. Compared to Liputan6, the summary label of Indosum is less abstractive, with novel 1-gram and novel 4-gram rates of 3.1% and 20.3%, respectively (Koto et al., 2020a). |  CC-BY-SA 4.0 |  2021 |  ind |  Crawling & human annotation |  18,773 |  sentences |  14083 train, 1880 validation, 2810 test
IndoTacos |  IndoTacos dataset is tax court verdict summary collected from perpajakan.ddtc,co.id. It contains 12k tax court summary with its verdict: mengabulkan seluruhnya, mengabulkan sebagian, menolak, mengabulkan, lain-lain. This legal document is spesific for Indonesia tax cases. |  CC-BY-NC-SA 4.0 |  2021 |  ind |  Machine generated / Crawling w/o curation |  12,291 |  documents |  No dataset split
Indo_MultiModal_CC_12M Changpinyo et al. (2021) |  Conceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training. Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M). Indo_MultiModal_CC_12M is the Indonesian language version. |  Custom222222https://github.com/google-research-datasets/conceptual-12m/blob/main/LICENSE |  2022 |  ind |  Machine generated / Crawling w/o curation |  1 |  GB |  No dataset split
Indo_MultiModal_LAION Schuhmann et al. (2021) |  Indo_MultiModal_LAION is a translated subset of the LAION-400M dataset with 70M image-text pairs specifically meant to be used for vision-language pre-training in Indonesian language. LAION-400M is a dataset with 400M English (image, text) pairs, filtered with OpenAI‘s CLIP by calculating the cosine similarity between the text and image embeddings and dropping those with a similarity below 0.3. The threshold of 0.3 had been determined through human evaluations and seemed to be a good heuristic for estimating semantic image-text-content matching. The image-text-pairs have been extracted from the Common Crawl web data dump and are from random web pages crawled between 2014 and 2021. More info for LAION-400M: https://laion.ai/blog/laion-400-open-dataset/. |  CC-BY 4.0 |  2022 |  ind |  Machine generated / Crawling w/o curation |  7 |  GB |  No dataset split
Indo_MultiModal_PMD_ID Singh et al. (2022) |  Introduced in the FLAVA paper, Public Multimodal Dataset (PMD) is a collection of publicly-available image-text pair datasets. PMD contains 70M image-text pairs in total with 68M unique images. The dataset contains pairs from Conceptual Captions, Conceptual Captions 12M, WIT, Localized Narratives, RedCaps, COCO, SBU Captions, Visual Genome and a subset of YFCC100M dataset. Indo_MultiModal_PMD_Indonesia is the Indonesian language version. |  CC-BY-4.0 |  2022 |  ind |  Machine generated / Crawling w/o curation |  15 |  GB |  0 train, 0 validation, 0 test
Indonesian Clickbait William and Sari (2020) |  The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo, Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii) 15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline. Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait. |  CC-BY 4.0 |  2020 |  ind |  Crawling & human annotation |  15,000 |  headlines |  No dataset split
Indonesian Frog Storytelling Corpus Moeljadi (2012) |  Indonesian written and spoken storytelling corpus, based on the twenty-eight pictures. |  Unknown |  2014 |  ind |  Unknown |  0 |  documents |  No dataset split
Indonesian Google Play Review |  Indonesian Google Play Review, dataset scrapped from e-commerce app on Google Play for sentiment analysis. |  CC-BY 4.0 |  2022 |  ind |  Machine generated / Crawling w/o curation |  10,041 |  sentences |  7028 train, 3012 validation, 0 test
Indonesian Hoax News Detection Pratiwi et al. (2017) |  Indonesian Hoax News Detection is a dataset for hoax news detection. 600 Data are retrieved in Indonesian language with 372 valid news and 228 fake news.All data are manually labelled. |  CC-BY 4.0 |  2018 |  ind |  Human generation & curation |  600 |  documents |  No dataset split
Indonesian Poem Tweets |  Indonesian Poem tweets is dataset crawled from Twitter. The purpose of this data is to create text generation model for short text and make sure they are all coherence and rhythmic |  CC-BY 4.0 |  2022 |  ind |  Machine generated / Crawling w/o curation |  16,427 |  tweets |  No dataset split
Indonesian Stance Jannati et al. (2018) |  ID Stance is a collection of Kompasiana articles that match with a pre-defined list of Indonesian politician names. Each article possesses a stance towards a candidate entity and election event, determined by annotators. Since the task is framed as a binary classification task, articles with no stance (neutral) are excluded from the gold-standard set. |  CC-BY-NC-SA 4.0 |  2018 |  ind |  Crawling & human annotation |  337 |  documents |  No dataset split
Indonesian WSD Mahendra et al. (2018) |  Indonesian WSD is a word sense dissambiguation dataset automatically collected using CrossLingual WSD (CLWSD) approach by utilizing WordNet and parallel corpus GIZA++. The monolingual WSD model is built from training data and it is used to assign the correct sense to any previously unseen word in a new context. The dataset covers 6 commonly ambiguous words, i.e., alam, atas, kayu, anggur, perdana, and dasar, with a total of 2416 sentences. |  Unknown |  2018 |  ind |  Machine generated / Crawling w/o curation |  2,416 |  sentences |  No dataset split
Indonesian general domain MT En-Id Guntara et al. (2020) |  For the general domain, both Tatoeba and TALPCo are manually curated, but their sentences (especially Tatoeba) are very short compared to Wikimatrix. Therefore, for these two datasets, we do a random split involving all datasets in the domain for validation and testing, each having 2000 unique pairs not present in the training set. For the general domain, we mix shorter sentences from TALPCo and the longer ones from Wikimatrix as our validation and test data. We observe that Tatoeba has similar types of high-quality sentences like TALPCo has, albeit shorter. Therefore we choose TALPCo to be in the validation and test sets instead, because longer sentences mean more difficult and meaningful evaluation. Tatoeba dataset contains short sentences. However, they contain high-quality full-sentence pairs with precise translation and is widely used in previous work in other languages (Artetxe and Schwenk, 2019b). Due to its simplicity, we do not use Tatoeba as our test and validation sets. We find that the Wikipedia scraper for Wikimatrix is faulty in some cases, causing some noise coming from unfiltered markup tags. |  CC-BY-SA 4.0 |  2020 |  eng, ind |  Human generation & curation |  1,811,300 |  sentences |  1729472 train, 2000 validation, 2000 test
Indonesian religious domain MT En-Id Guntara et al. (2020) |  Religious domain consists of religious manuscripts or articles. These articles are different from news as they are not in a formal, informative style. Instead, they are written to advocate and inspire religious values, often times citing biblical or quranic anecdotes. The Tanzil dataset is a Quran translation dataset which has a relatively-imbalanced sentence length between the two languages, evidenced in Table 2, where an average Indonesian sentence in this dataset is about 50% longer than an average English one. Furthermore, an average pair of sentences in this dataset would, on average, have one of them twice as long as the other. However, we still decide to include the dataset in the domain to avoid overfitting because the remaining datasets are all about Christianity. Another interesting property in the religion domain corpus is the localized names, for example, David to Daud, Mary to Maryam, Gabriel to Jibril, and more. In contrast, entity names are usually kept unchanged in other domains. We also find quite a handful of Indonesian translations of JW300 are missing the end sentence dot (.), even though the end sentence dot is present in their English counterpart. Lastly, we also find some inconsistency in the transliteration, for example praying is sometimes written as salat or shalat, or repentance as tobat or taubat. |  CC-BY-SA 4.0 |  2020 |  eng, ind |  Human generation & curation |  1,068,400 |  sentences |  579544 train, 5000 validation, 4823 test
JATI Moeljadi (2017) |  JATI is a treebank built from a subset of parsed dictionary definition sentences. The main data for this study comes from the fifth edition of Kamus Besar Bahasa Indonesia (KBBI) (Amalia, 2016), the official and the most comprehensive dictionary for the Indonesian language. The dictionary definition sentences are parsed using the Indonesian Resource Grammar (INDRA) (Moeljadi, Bond, and Song 2015), a computational grammar for Indonesian in the Head-Driven Phrase Structure Grammar (HPSG) framework (Sag, Wasow, and Bender, 2003). JATI will be employed to build an ontology, in which knowledge is extracted from the semantic representation in Minimal Recursion Semantics (MRS) (Copestake et al., 2005). |  Unknown |  2017 |  ind |  Human generation & curation |  1,253 |  sentences |  No dataset split
JV-ID ASR Kjartansson et al. (2018) |  This dataset contains transcribed audio data for Javanese. The dataset consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The dataset has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia. |  CC-BY-SA 4.0 |  2018 |  jav |  Human generation & curation |  185,076 |  utterances |  No dataset split
JV-ID TTS Sodimana et al. (2018) |  This dataset contains high-quality transcribed audio data for Javanese. The dataset consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The dataset has been manually quality checked, but there might still be errors.This dataset was collected by Google in collaboration with Gadjah Mada University in Indonesia. |  CC-BY-SA 4.0 |  2018 |  ind, jav |  Human generation & curation |  5,800 |  sentences |  No dataset split
JaDi-Ide Hidayatullah et al. (2020) |  The dataset is collected from Twitter. We named the dataset as Javanese dialect identification (JaDi-Ide). The dialect is classified into Standard Javanese, Ngapak Javanese, and East Javanese dialects. |  Unknown |  2020 |  jav |  Crawling & human annotation |  16,000 |  tweets |  No dataset split
KEPS Mahfuzh et al. (2019) |  KEPS is a keyphrase extraction dataset consists of text from Twitter discussing banking products and services and is written in the Indonesian language. A phrase containing important information is considered a keyphrase. Text may contain one or more keyphrases since important phrases can be located at different positions. The dataset follows the IOB chunking format, which represents the position of the keyphrase. |  CC-BY-SA 4.0 |  2019 |  ind |  Crawling & human annotation |  1,247 |  documents |  1000 train, 247 test
KaWAT Kurniawan (2019) |  We introduced KaWAT (Kata Word Analogy Task), a new word analogy task dataset for Indonesian. We evaluated on it several existing pretrained Indonesian word embeddings and embeddings trained on Indonesian online news corpus. We also tested them on two downstream tasks and found that pretrained word embeddings helped either by reducing the training epochs or yielding significant performance gains. |  Apache 2.0 |  2019 |  ind |  Human generation & curation |  34,000 |  sentence pairs |  No dataset split
Kamus Alay Salsabila et al. (2018) |  We provide a lexicon for text normalization of Indonesian colloquial words. We gathered 3,592 unique colloquial words-also known as bahasa alay -and manually annotated them with the normalized form. We built this lexicon from Instagram comments provided by Septiandri & Wibisono (2017). |  Unknown |  2018 |  ind |  Human generation & curation |  3,592 |  tokens |  No dataset split
Karonese dataset Karo et al. (2022) |  Karonese dataset consist karonese text and the label (positive, negaitive or neutra). karonese text comes from multi domain social media, such us facebook, twitter, Instagram and Youtube |  Unknown |  2022 |  btx |  Crawling & human annotation |  1,001 |  sentences |  0 train, 0 validation, 0 test
Kethu Arwidarasti et al. (2019) |  Kethu is a constituency treebank derived from Universitas Indonesia Constituency Treebank (UI-CTB) corpus Kethu converts UI-CTB treebank format into the widely accepted Penn Treebank format by adjusting the bracketing format for compound words as well as the POS tagset according to the Penn Treebank format. In addition, word segmentation and POS tagging of a number of tokens are also revised from the original UI-CTB corpus. |  Unknown |  2019 |  ind |  Machine generated w/ human curation |  1,030 |  sentences |  No dataset split
KoPI-CC (Korpus Perayapan Indonesia) |  KoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian Only Extract from Common Crawl snapshots ,each snapshots get extracted using ungoliant oscar tools and get extra filtering using deduplication technique (Exact Hash Dup and Minhash LSH) |  CC0 |  2022 |  ind |  Machine generated / Crawling w/o curation |  106 |  GB |  No dataset split
KoPI-CC_News |  KoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio and filter using fasttext |  CC0 |  2022 |  ind |  Machine generated / Crawling w/o curation |  4 |  GB |  No dataset split
KoPI-NLLB |  KopI(Korpus Perayapan Indonesia)-NLLB, is Indonesian family language(aceh,bali,banjar,indonesia,jawa,minang,sunda) only extracted from NLLB Dataset each language set also filtered using some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup |  ODC-BY |  2022 |  ind, sun, jav, min, ban, bjn, ace |  Machine generated / Crawling w/o curation |  18 |  GB |  No dataset split
Korpus Nusantara Sujaini (2020) |  The dataset is a combination of multiple machine translation works from the author, Herry Sujaini, covering Indonesian to 25 local dialects in Indonesia. Since not all dialects have ISO639-3 standard coding, as agreed with Pak Herry , we decided to group the dataset into the closest language family, i.e., Javanese, Dayak, Buginese, Sundanese, Madurese, Banjar, Batak Toba, Hakka (Khek), Malay, Minangkabau, and Min Nan (Teochew). |  Unknown |  2022 |  ind, sun, jav, min, mad, bbc, bug, msa, xdy, hak, nan |  Human generation & curation |  68,856 |  sentence pairs |  No dataset split
LibriVox-Indonesia Wirawan (2022) |  The LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the public domain audiobooks LibriVox. We collected only languages in Indonesia for this dataset. The original LibriVox audiobooks or sound files’ duration varies from a few minutes to a few hours. Each audio file in the speech dataset now lasts from a few seconds to a maximum of 20 seconds. |  CC0 |  2022 |  ind, sun, jav, min, bug, ban, ace |  Machine generated / Crawling w/o curation |  7,815 |  utterances |  No dataset split
Liputan6 Summarization Koto et al. (2020a) |  The Liputan6 dataset was crawled from an online Indonesian news portal, which covers a wide range of topics, such as politics, sport, technology, business, health, and entertainment. There are two different experimental settings for Liputan6: Canonical, which includes all the test samples, and Xtreme, which only includes test samples with more than 90% novel 4-grams in the summary label. |  CC-BY-SA 4.0 |  2021 |  ind |  Crawling & human annotation |  224,637 |  sentences |  193883 train, canonical: 10972 validation, 10972 test), extreme: 4948 validation, 3862 test),
Local ID Abusive Putri et al. (2021) |  Local ID Abusive is dataset aimed to be used for abusive and hate speech detection available in Javanese and Sundanese. The Javanese and Sundanese were annotated manually by annotator from each region. The annotation process involved multiple-step processes. It was carried by two annotators for each language, after an initial step where the guidelines were discussed and refined to reach unanimous comprehension. The annotation process gives 3449 and 2207 tweets for Javanese and Sundanese dataset respectively with 100% agreement. |  Unknown |  2021 |  sun, jav |  Crawling & human annotation |  5,656 |  sentences |  No dataset split
MaRVL Liu et al. (2021a) |  Multicultural Reasoning over Vision and Language (MaRVL) is a dataset based on an ImageNet-style hierarchy representative of many languages and cultures (Indonesian, Mandarin Chinese, Swahili, Tamil, and Turkish). The selection of both concepts and images is entirely driven by native speakers. Afterwards, we elicit statements from native speakers about pairs of images. The task consists in discriminating whether each grounded statement is true or false. The present file contains all the dataset images and annotations. |  CC-BY 4.0 |  2021 |  ind |  Crawling & human annotation |  1,128 |  image, image, concept, caption |  No dataset split
MinangNLP MT Koto and Koto (2020) |  MinangNLP MT is a machine translation dataset generated from two sentiment analysis dataset by manual translation to Minangkabau language |  MIT |  2020 |  ind, min |  Crawling & human annotation |  5,000 |  sentences |  11,571 train, 1600 validation, 3200 test
MultiLexNorm van der Goot et al. (2021a) |  MultiLexNorm is multilingual benchmark dataset for lexical normalization task for 12 languages, including Indonesian-English (code-mixed). Lexical normalization is the task of transforming an utterance into its standard form, word by word, including both one-to-many (1-n) and many-to-one (n-1) replacements. ID-EN dataset actually originates from Barik et.al. (2019) work. However, there is preprocessing work upon the original dataset. |  CC-BY-NC-SA 4.0 |  2021 |  ind, eng |  Crawling & human annotation |  13,949 |  tokens |  13950 train, 4810 validation, 4367 test
Multilingual Open Relations Faruqui and Kumar (2015) |  This dataset provides the set of automatically extracted relations obtained using cross-lingual annotation projection method. The data covers 61 languages, including Indonesian. Relation extraction is the task of assigning a semantic relationship between a pair of arguments. For example, from the sentence Soekarno lahir di Jawa Timur, the relation <Soekarno, born_in, Jawa Timur> is expected to be extracted. |  Unknown |  2015 |  ind |  Machine generated / Crawling w/o curation |  1,876 |  relations |  No dataset split
NER UGM (IndoLEM split) Fachri (2014) |  NER UGM is a named entity recognition dataset collected by UGM. We use IndoLEM split for the dataset. |  CC BY-SA 3.0 |  2014 |  ind |  Crawling & human annotation |  2,343 |  sentences |  1687 train, 187 validation, 469 test
NER UI (IndoLEM split) Gultom and Wibowo (2017) |  NER UI is a named entity recognition dataset collected by UI. We use IndoLEM split for the dataset. |  CC BY-SA 3.0 |  2017 |  ind |  Human generation & curation |  2,125 |  sentences |  1530 train, 170 validation, 425 test
NERGrit |  NER Grit dataset is a NER dataset taken from the Grit-ID repository, and the labels are spans in IOB chunking representation. The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and ORGANIZATION (name of organization). |  custom |  2020 |  ind |  Unknown |  17,437 |  sentences |  12518 train, 2521 validation, 2398 test
NERP (IndoNLU Split) Hoesen and Purwarianti (2018) |  NERP is a NER dataset which is collected from several Indonesian news websites, labelled with 5 entity classes: PER (name of person), LOC (name of location), IND (name of product or brand), EVT (name of the event), and FNB (name of food and beverage). |  CC-BY-SA 4.0 |  2018 |  ind |  Human generation & curation |  8,400 |  sentences |  6720 train, 840 validation, 840 test
NLLB Seed NLLB Team et al. (2022) |  NLLB Seed is a set of professionally-translated sentences in the Wikipedia domain. Data for NLLB-Seed was sampled from Wikimedia’s List of articles every Wikipedia should have, a collection of topics in different fields of knowledge and human activity. NLLB-Seed consists of around six thousand sentences in 39 languages. NLLB-Seed is meant to be used for training rather than model evaluation. Due to this difference, NLLB-Seed does not go through the human quality assurance process present in FLORES-200. |  CC-BY-NC 4.0 |  2022 |  eng, ace, bjn, bug |  Crawling & human annotation |  30,965 |  sentence pairs |  No dataset split
Netifier |  Netifier dataset is a collection of scraped posts on famous social media sites in Indonesia, such as Instagram, Twitter, and Kaskus aimed to do multi-label toxicity classification. The dataset consists of 7,773 texts. The author manually labelled 7k samples into 4 categories: pornography, hate speech, racism, and radicalism. |  CC-BY-NC-SA 4.0 |  2018 |  ind |  Crawling & human annotation |  7,773 |  tweets |  6995 train, 0 validation, 778 test
News En-Id MT Moeljadi and Aminullah (2020) |  News En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from news translation dataset (Guntara et al., 2020). The news dataset (Guntara et al., 2020) is collected from multiple sources: Pan Asia Networking Localization (PANL), Bilingual BBC news articles, Berita Jakarta, and GlobalVoices |  CC-BY-SA 4.0 |  2021 |  eng, ind |  Crawling & human annotation |  44,325 |  sentences |  38469 train, 1953 validation, 1954 test
NusaX MT Winata et al. (2023) |  The first-ever parallel resource for 10 low-resource languages in Indonesia. |  CC-BY-SA 4.0 |  2022 |  ind, ace, jav, sun, min, bug, bbc, ban, nij, mad, bjn, eng |  Human generation & curation |  132,000 |  sentence pairs |  500 train, 100 dev, 400 test
NusaX Sentiment Winata et al. (2023) |  The first-ever parallel resource for 10 low-resource languages in Indonesia. |  CC-BY-SA 4.0 |  2022 |  ind, ace, jav, sun, min, bug, bbc, ban, nij, mad, bjn, eng |  Human generation & curation |  12,000 |  sentences |  500 train, 100 dev, 400 test
OJW Moeljadi and Aminullah (2020) |  OJW is abbreviation of Old Javanese Wordnet. |  Unknown |  2020 |  kaw |  Unknown |  5,038 |  tokens |  No dataset split
PANL BPPT Riza and Hakim (2009) |  Parallel Text Corpora for Multi-Domain Translation System created by BPPT (Indonesian Agency for the Assessment and Application of Technology) for PAN Localization Project (A Regional Initiative to Develop Local Language Computing Capacity in Asia). The dataset contains around 24K sentences divided in 4 difference topics (Economic, international, Science and Technology and Sport). |  Unknown |  2009 |  eng, ind |  Crawling & human annotation |  24,000 |  sentences |  No dataset split
POSP (IndoNLU Split) Hoesen and Purwarianti (2018) |  POSP is an Indonesian part-of-speech tagging (POS) dataset collected from Indonesian news websites. The dataset consists of around 8000 sentences with 26 POS tags following the Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention. |  CC-BY-SA 4.0 |  2018 |  ind |  Human generation & curation |  8,400 |  sentences |  6720 train, 840 validation, 840 test
ParaCotta Aji et al. (2021) |  ParaCotta is a synthetic parallel paraphrase corpus generated from monolingual data and a neural machine translation system. Multiple translations were generated using beam search, and then paraphrase pairs were selected based on the lexical difference determined by their sentence BLEU. |  Unknown |  2021 |  ind, eng |  Machine generated / Crawling w/o curation |  6,000,000 |  sentence pairs |  No dataset split
Parallel: Indonesian - Lampung Nyo Abidin and Ahmad (2021) |  Parallel Indonesian - Lampung Nyo corpus is constructed from documents taken from the Lampung language book for elementary and junior high school levels in the province of Lampung. The document data that has been collected will then be manually typed to be made into a parallel corpus in Indonesian – Lampung dialect of nyo and mono corpus in Lampung dialect of nyo. There are 3000 parallel corpus sentences collected in Indonesian - Lampung dialect of nyo and 3000 mono corpus sentences in Indonesian - Lampung dialect of nyo. |  Unknown |  2021 |  abl, ind |  Crawling & human annotation |  3,000 |  sentences |  No dataset split
PoSTagged Sundanese Monolingual Corpus Ardiyanti Suryani et al. (2022a) |  The dataset consists of 3616 Sundanese sentences collected from several Sundanese online magazine (Mangle, Dewan Dakwah Jabar, and Balebat). The dataset is mannualy annotated with Part of Speech label by several Sundanese Language faculty students from UPI Bandung. |  CC0 |  2015 |  sun |  Crawling & human annotation |  3,616 |  sentences |  No dataset split
SQuAD ID Muis and Purwarianti (2020) |  Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. This version is translated version of SQuAD to Indonesian Language. |  Unknown |  2020 |  ind |  Machine generated / Crawling w/o curation |  124,631 |  documents |  102657 train, 11407 validation, 10567 test
STIF-Indonesia Wibowo et al. (2020) |  STIF-Indonesia is an Indonesian text style transfer dataset collected from Twitter. The style transfer is done from informal style to a formal style. STIF-Indonesia consists of 52.5k sentences with 2.5k of which is manually annotated. |  MIT |  2020 |  ind |  Crawling & human annotation |  52.5k |  sentences |  1922 train, 214 validation, 363 test
SU-ID ASR Kjartansson et al. (2018) |  This dataset contains transcribed audio data for Sundanese. The dataset consists of wave files, and a TSV file. The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file. The dataset has been manually quality checked, but there might still be errors. This dataset was collected by Google in Indonesia. |  CC-BY-SA 4.0 |  2018 |  sun |  Human generation & curation |  220,000 |  sentences |  No dataset split
SU-ID TTS Sodimana et al. (2018) |  This dataset contains high-quality transcribed audio data for Sundanese. The dataset consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number. The dataset has been manually quality checked, but there might still be errors. This dataset was collected by Google in collaboration with Universitas Pendidikan Indonesia. |  CC-BY-SA 4.0 |  2018 |  sun |  Human generation & curation |  4,213 |  utterances |  No dataset split
Sampiran Siallagan and Alfina (2023) |  Sampiran is a dataset for pantun generation. It consists of 7.8K Indonesian pantun, collected from various sources (online). Pantun is a traditional Malay poem consisting of four lines: two lines of deliverance and two lines of message. This dataset filtered the gathered Pantun to follow the general rules of Pantun; four lines with ABAB rhyme and eight to twelve syllables per line. |  AGPL-3.0 |  2023 |  ind |  Crawling & human annotation |  7,879 |  sentences |  No dataset split
Semeval STS Indo |  SemEval is a series of international natural language processing (NLP) research workshops whose mission is to advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a range of increasingly challenging problems in natural language semantics. This is a translated version of SemEval Dataset from 2012-2016 for Semantic Textual Similarity Task to Indonesian language. |  Unknown |  2019 |  ind, eng |  Machine generated / Crawling w/o curation |  12,901 |  sentence pairs |  10293 train, 0 validation, 2608 test
Singgalang Alfina et al. (2017b) |  Singgalang is an automatically tagged Indonesian NER dataset collected from Wikipedia articles which uses DBpedia as the reference of the entity type. The dataset used expanded DBpedia of MDEE_Gazetteer as the reference to label the token and contains 48,957 sentences. |  Unknown |  2017 |  ind |  Machine generated / Crawling w/o curation |  48,957 |  sentences |  No dataset split
SmSA Purwarianti and Crisdayanti (2019) |  SmSA is a sentence-level sentiment analysis dataset consisting of of comments and reviews in Indonesian obtained from multiple online platforms with 3 possible sentiments:positive, negative, and neutral. The text was crawled and then annotated by several Indonesian linguists to construct this dataset. |  CC-BY-SA 4.0 |  2020 |  ind |  Crawling & human annotation |  12,760 |  sentences |  11000 train, 1260 validation, 500 test
Sundanese Twitter Dataset for Emotion Putra et al. (2020) |  Sunda Emotion dataset gathered from Twitter API between January and March 2019 with 2518 tweets in total. The tweets filtered by using some hashtags which are represented Sun-danese emotion, for instance, #persib, #corona, #saredih, #nyakakak, #garoblog, #sangsara, #gumujeng, #bungah, #sararieun, #ceurik, and #hariwang. This dataset contains four distinctive emotions: anger, joy, fear, and sadness.Each tweet is annotated using related emotion. For data validation, we consulted a Sundanese language teacher for expert validation |  Unknown |  2020 |  sun |  Crawling & human annotation |  2,518 |  sentences |  No dataset split
Sundanese-Indonesian Parallel Corpus Ardiyanti Suryani et al. (2022b) |  The dataset consists of 3616 Sundanese sentences taken from a Sundanese online magazing (Mangle), Dewan Dakwah Jabar, and Balebat. The dataset is manually translated to Indonesian language by several Sundanese Language faculty students from UPI Bandung. |  CC0 |  2015 |  ind, sun |  Human generation & curation |  3,616 |  sentence pairs |  No dataset split
TALPCo Nomoto et al. (2018) |  The TUFS Asian Language Parallel Corpus (TALPCo) is an open parallel corpus consisting of Japanese sentences and their translations into Korean, Burmese (Myanmar; the official language of the Republic of the Union of Myanmar), Malay (the national language of Malaysia, Singapore and Brunei), Indonesian, Thai, Vietnamese and English |  CC-BY 4.0 |  2018 |  ind, eng, kor, jpn, vie, tha, msa |  Unknown |  0 |  sentences |  No dataset split
TED En-Id MT Qi et al. (2018) |  TED En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from TED translation dataset (Qi et al., 2018), collected from TED talk transcripts |  CC-BY-SA 4.0 |  2021 |  eng, ind |  Crawling & human annotation |  93,262 |  sentences |  87406 train, 2677 validation, 3179 test
TICO-19 Anastasopoulos et al. (2020) |  TICO-19 (Translation Initiative for COVID-19) is sampled from a variety of public sources containing COVID-19 related content, representing different domains (e.g., news, wiki articles, and others). TICO-19 includes 30 documents (3071 sentences, 69.7k words) translated from English into 36 languages: Amharic, Arabic (Modern Standard), Bengali, Chinese (Simplified), Dari, Dinka, Farsi, French (European), Hausa, Hindi, Indonesian, Kanuri, Khmer (Central), Kinyarwanda, Kurdish Kurmanji, Kurdish Sorani, Lingala, Luganda, Malay, Marathi, Myanmar, Nepali, Nigerian Fulfulde, Nuer, Oromo, Pashto, Portuguese (Brazilian), Russian, Somali, Spanish (Latin American), Swahili, Congolese Swahili, Tagalog, Tamil, Tigrinya, Urdu, Zulu. |  CC0 |  2020 |  ind, eng, ara, spa, fra, hin, por, rus, zho |  Machine generated w/ human curation |  21,497 |  sentence pairs |  0 train, 6797 validation, 14700 test
TITML-IDN Lestari (2006) |  TITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected to build a pioneering Indonesian Large Vocabulary Continuous Speech Recognition (LVCSR) System. In order to build an LVCSR system, high accurate acoustic models and large-scale language models are essential. Since Indonesian speech corpus was not available yet, we tried to collect speech data from 20 Indonesian native speakers (11 males and 9 females) to construct a speech corpus for training the acoustic model based on Hidden Markov Models (HMMs). A text corpus which was collected by ILPS, Informatics Institute, University of Amsterdam, was used to build a 40K-vocabulary dictionary and a n-gram language model. |  For research purposes only. If you use this corpus, you have to cite (Lestari et al, 2006). |  2006 |  ind |  Human generation & curation |  6,679 |  utterances |  No dataset split
TUFS Indonesia Constituency Tree Nomoto (2022) |  TUFS Indonesia Constituency Tree is annotated dataset for Indonesian language constituency tree. |  CC-BY 4.0 |  2022 |  ind, msa |  Human generation & curation |  1,385 |  sentences |  No dataset split
TermA Fernando et al. (2019) |  The TermA span-extraction dataset is collected from the hotel aggregator platform, AiryRooms. The dataset consists of thousands of hotel reviews, which each contain a span label for aspect and sentiment words representing the opinion of the reviewer on the corresponding aspect. The labels use Inside-Outside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment. |  CC-BY-SA 4.0 |  2019 |  ind |  Human generation & curation |  5,000 |  documents |  3000 train, 1000 validation, 1000 test
Toxicity-200 NLLB Team et al. (2022) |  Toxicity-200 is a wordlist to detect toxicity in 200 languages. It contains files that include frequent words and phrases generally considered toxic because they represent: 1) frequently used profanities; 2) frequently used insults and hate speech terms, or language used to bully, denigrate, or demean; 3) pornographic terms; and 4) terms for body parts associated with sexual activity. |  CC-BY-NC 4.0 |  2022 |  ind, ace, bjn, bug, jav |  Human generation & curation |  226 |  phrases |  No dataset split
TyDiQA Id Cahyawijaya et al. (2021b) |  For the question answering task, we use the TyDiQA (Clark et al., 2020) dataset. This dataset is collected from Wikipedia articles with human-annotated question and answer pairs covering 11 languages. The question-answer pairs are collected for each language without using translation services. We use the Indonesian data from the secondary Gold passage task of the TyDiQA dataset. As the original dataset only provides training and validation sets, we randomly split off 15% of the training data and use it as the test set. |  CC-BY-SA 4.0 |  2021 |  ind |  Human generation & curation |  6,267 |  sentences |  4847 train, 565 validation, 855 test
UD_Indonesian-CSUI Alfina et al. (2020) |  The UD_Indonesian-CSUI is a dependency treebank in Indonesian in the CoNLL-U format. It was converted from a consituency treebank (Kethu) while Kethu was also converted from another consituency treebank (IDN treebank). Currently, this treebank, consist of 1030 sentences. |  MIT |  2020 |  ind |  Machine generated w/ human curation |  1,030 |  sentences |  656 train, 0 validation, 374 test
UD_Indonesian-GSD McDonald et al. (2013) |  UD_Indonesian-GSD is is an Indonesian-GSD treebank dataset originally converted from the content head version of the universal dependency treebank v2.0 (legacy) in 2015. In order to comply with the latest Indonesian annotation guidelines, the treebank has undergone a major revision between UD releases v2.8 and v2.9 (2021). |  CC BY-SA 3.0 |  2013 |  ind |  Human generation & curation |  5,593 |  sentences |  4477 train, 559 validation, 557 test
UD_Indonesian-PUD Alfina et al. (2019) |  An Indonesian dependency treebank that is part of a collection of 18 Parallel Universal Dependencies (PUD) treebanks. |  CC BY-SA 3.0 |  2019 |  ind |  Human generation & curation |  1,000 |  sentences |  10 fold cross validation according to UD standard
UKARA 1.0 Challenge Septiandri and Winatmoko (2020) |  Ukara 1.0 Challenge dataset is a dataset for automatic short answer scoring system which is a collaboration project between FMIPA UGM and PUSPENDIK, Ministry of Education and Culture of Indonesia. It was intended to build supervised machine learning approach which is able to assign a score to student’s answer. The student’s answer usually consists of maximum 2-3 sentences. |  Unknown |  2020 |  ind |  Human generation & curation |  2,861 |  sentences |  268 train, 215 validation, 855 test
Unimorph ID Pimentel et al. (2021) |  The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carried by the lemma, and by a rendering of its inflectional form in terms of a bundle of morphological features from our schema |  Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0) |  2021 |  ind |  Crawling & human annotation |  27,714 |  forms |  70% train, 10% validation, 20% test
VoxLingua107 Valk and Alumäe (2021) |  VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages, including Indonesian, Javanese, and Sundanese. |  CC-BY 4.0 |  2021 |  ind, sun, jav |  Crawling & human annotation |  157 |  hours |  No dataset split
WReTe Setya and Mahendra (2023) |  WReTe is an entailment dataset which consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between the pairs. The data are labeled as entailed when the meaning of the second sentence can be derived from the first one, and not entailed otherwise. |  CC-BY-SA 4.0 |  2018 |  ind |  Crawling & human annotation |  450 |  sentence pairs |  300 train, 50 validation, 100 test
WikiAnn Pan et al. (2017) |  We developed a simple yet effective framework that can extract names from 282 languages and link them to an English KB. This framework follows a fully automatic training and testing pipeline, without the needs of any manual annotations or knowledge from native speakers. We release the following resources for each of these 282 languages: “silver-standard” name tagging and linking annotations with multiple levels of granularity, morphology analyzer if it’s a morphologically-rich language, and an end to-end name tagging and linking system. |  Attribution License (ODC-By) |  2017 |  ind, eng, sun, jav, min, bug, bjn, tpi, ace, tdt, msa, jav-bms |  Machine generated / Crawling w/o curation |  254,240 |  Number of name mentions |  No dataset split
WikiLingua Ladhak et al. (2020) |  We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of crosslingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow12, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article summary alignments across languages by aligning the images that are used to describe each how-to step in an article. |  CC-BY-NC-SA 3.0 |  2020 |  ind, eng |  Crawling & human annotation |  47,511 |  article-summary pairs |  No dataset split
X-FACT Gupta and Srikumar (2021) |  The largest publicly available multilingual dataset for factual verification of naturally existing real world claims. The dataset contains short statements in 25 languages and is labeled for veracity by expert fact-checkers. The dataset includes a multilingual evaluation benchmark that measures both out-of-domain generalization, and zero-shot capabilities of the multilingual models. |  MIT |  2021 |  ind, eng |  Crawling & human annotation |  3,548 |  evidences-links-claim |  2231 train, 297 validation, 448 test
XCOPA Ponti et al. (2020) |  Cross-lingual Choice of Plausible Alternatives (XCOPA), a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages, including Indonesian. The causal commonsense reasoning task consists two task variations, forward causal reasoning and backward reasoning. In forward causal reasoning, given the premise sentence, the model is asked to predict most reasonable result from two alternative. In backward causal reasoning, the model is asked to predict what causes the premise happens |  CC-BY 4.0 |  2021 |  ind |  Human generation & curation |  600 |  sentences |  0 train, 100 validation, 500 test
XL-Sum Hasan et al. (2021) |  XL-Sum is a comprehensive and diverse dataset comprising 1 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics. The dataset covers 44 languages, including Indonesian. |  CC-BY-NC-SA 4.0 |  2021 |  ind |  Crawling & human annotation |  47,802 |  document-summary pairs |  38242 train, 4780 validation, 4780 test
XPersona Id Lin et al. (2021) |  XPersona is a open-domain dialogue system on 7 languages including Indonesia. The test set is manually translated by exper annotators, while the training and validation set isareautomatically translated from the persona chat dataset with an additional manual keyword correction phase. |  CC-BY-SA 4.0 |  2021 |  ind |  Machine generated w/ human curation |  17,866 |  utterances |  16878 train, 484 validation, 484 test
id-en-code-mixed Barik et al. (2019) |  This dataset contain 825 tweet instances of Indonesian-English, corresponding to four NLP tasks, i.e., tokenization, language identification, lexical normalization, and word translation. Data for lexical normalization task is curated in MultiLexNorm (already in Nusa Catalogue), but other tasks are not. Tokenization for social media data is not as trivial as splitting the token using white space delimiter. In this data, language identification is performed in token-level granularity. |  CC-BY-NC-SA 4.0 |  2019 |  ind, eng |  Crawling & human annotation |  22,736 |  tokens |  No dataset split
Indo Wiki Paralel Corpora Trisedya and Inastra (2014) |  Manually aligned parallel corpora from Wikipedia |  Unknown |  2014 |  ind, sun, jav, min |  Crawling & human annotation |  2,422 |  sentence pairs |  No dataset split
indo-law Nuranti et al. (2022) |  This dataset consists of Indonesian court decision documents for general criminal cases that have been annotated for the document sections. The documents were taken from the website of the Indonesian Supreme Court Decision. There are 22,630 documents with xml format in this dataset, which each contains 11 tags that enclose the annotated sections of the court decision documents. |  Unknown |  2022 |  ind |  Crawling & human annotation |  22,630 |  documents |  No dataset split
xSID van der Goot et al. (2021b) |  We introduce XSID, a new benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect. |  CC-BY-SA 4.0 |  2021 |  ind, eng |  Crawling & human annotation |  5,370,460 |  sentences |  No dataset split