| | --- |
| | license: cc-by-nc-4.0 |
| | language: |
| | - es |
| | - eu |
| | pretty_name: EuskañolDS |
| | size_categories: |
| | - 1K<n<10K |
| | dataset_info: |
| | - config_name: eu |
| | features: |
| | - name: premise |
| | dtype: string |
| | - name: hypothesis |
| | dtype: string |
| | - name: label |
| | dtype: |
| | class_label: |
| | names: |
| | '0': entailment |
| | '1': neutral |
| | '2': contradiction |
| | - config_name: eu_mt |
| | features: |
| | - name: premise |
| | dtype: string |
| | - name: hypothesis |
| | dtype: string |
| | - name: label |
| | dtype: |
| | class_label: |
| | names: |
| | '0': entailment |
| | '1': neutral |
| | '2': contradiction |
| | - config_name: eu_native |
| | features: |
| | - name: premise |
| | dtype: string |
| | - name: hypothesis |
| | dtype: string |
| | - name: label |
| | dtype: |
| | class_label: |
| | names: |
| | '0': entailment |
| | '1': neutral |
| | '2': contradiction |
| | configs: |
| | - config_name: eu |
| | data_files: |
| | - split: train |
| | path: xnli.train.eu.mt.tsv |
| | - split: validation |
| | path: xnli.dev.eu.tsv |
| | - split: test |
| | path: xnli.test.eu.tsv |
| | - config_name: eu_mt |
| | data_files: |
| | - split: train |
| | path: xnli.train.eu.mt.tsv |
| | - split: validation |
| | path: xnli.dev.eu.mt.tsv |
| | - split: test |
| | path: xnli.test.eu.mt.tsv |
| | - config_name: eu_native |
| | data_files: |
| | - split: test |
| | path: xnli.test.eu.native.tsv |
| | --- |
| | # Dataset Card for XNLIeu |
| |
|
| | <!-- Provide a quick summary of the dataset. --> |
| |
|
| | XNLIeu is an extension of [XNLI](https://huggingface.co/datasets/xnli) translated from English to **Basque**. It has been designed as a cross-lingual dataset for the Natural Language Inference task, a text-classification task that consists on classifying pairs of sentences, a premise and a hypothesis, according to their semantic relation out of three possible labels: entailment, contradiction and neutral. |
| |
|
| | ## Dataset Details |
| |
|
| | ### Dataset Description |
| |
|
| | <!-- Provide a longer summary of what this dataset is. --> |
| | XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages. |
| | We expand XNLI to include Basque, a low-resource language that can greatly benefit from transfer-learning approaches. |
| | The new dataset, dubbed XNLIeu, has been developed by first machine-translating the English XNLI corpus into Basque, followed by a manual post-edition step. |
| |
|
| | - **Language(s) (NLP):** Basque (eu) |
| | - **License:** XNLIeu is derived from XNLI and distributed under its same license. |
| |
|
| | ### Dataset Sources |
| |
|
| | <!-- Provide the basic links for the dataset. --> |
| |
|
| | - **Repository:** [Link to the GitHub Repository](https://github.com/hitz-zentroa/xnli-eu/) |
| | - **Paper:** [Link to the Paper](https://aclanthology.org/2024.naacl-long.234/) |
| |
|
| | ## Uses |
| |
|
| | XNLieu is meant as an cross-lingual evaluation dataset. It can be used in combination with the train sets of [XNLI](https://huggingface.co/datasets/xnli) for a cross-lingual zero-shot setting, and we provide a machine-translated train set in both "eu" and "eu_mt" splits to implement a translate-train setting. |
| | ## Dataset Structure |
| | The dataset has three subsets: |
| | - **eu**: XNLIeu, machine-translated and post-edited from English to Basque. |
| | - **eu_MT**: XNLIeu<sub>MT</sub>, a machine-translated version prior post-edition. |
| | - **eu_native**: An original, non-translated test set. |
| | ### Splits |
| | | name |train |validation|test| |
| | |-------------|-----:|---------:|---:| |
| | |eu |392702| 2490|5010| |
| | |eu_mt |392702| 2490|5010| |
| | |eu_native |- | - |621 | |
| | ### Dataset Fields |
| | All splits have the same fields: *premise*, *hypothesis* and *label*. |
| | - **premise**: a string variable. |
| | - **hypothesis**: a string variable. |
| | - **label**: a classification label, with possible values including entailment (0), neutral (1), contradiction (2). |
| | ### Dataset Instances |
| | An example from the "eu" split: |
| | ``` |
| | { |
| | "premise": "Dena idazten saiatu nintzen" |
| | "hypothesis": "Nire helburua gauzak idaztea zen.", |
| | "label": 0, |
| | } |
| | ``` |
| | ## Bias, Risks, and Limitations |
| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
| | The biases of this dataset have been studied and reported in the paper. |
| | <!--## Citation |
| | <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. |
| | RELLENAR--> |
| | **BibTeX:** |
| | ``` |
| | @inproceedings{heredia-etal-2024-xnlieu, |
| | title = "{XNLI}eu: a dataset for cross-lingual {NLI} in {B}asque", |
| | author = "Heredia, Maite and |
| | Etxaniz, Julen and |
| | Zulaika, Muitze and |
| | Saralegi, Xabier and |
| | Barnes, Jeremy and |
| | Soroa, Aitor", |
| | editor = "Duh, Kevin and |
| | Gomez, Helena and |
| | Bethard, Steven", |
| | booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)", |
| | month = jun, |
| | year = "2024", |
| | address = "Mexico City, Mexico", |
| | publisher = "Association for Computational Linguistics", |
| | url = "https://aclanthology.org/2024.naacl-long.234", |
| | pages = "4177--4188", |
| | abstract = "XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages. In this paper, we expand XNLI to include Basque, a low-resource language that can greatly benefit from transfer-learning approaches. The new dataset, dubbed XNLIeu, has been developed by first machine-translating the English XNLI corpus into Basque, followed by a manual post-edition step. We have conducted a series of experiments using mono- and multilingual LLMs to assess a) the effect of professional post-edition on the MT system; b) the best cross-lingual strategy for NLI in Basque; and c) whether the choice of the best cross-lingual strategy is influenced by the fact that the dataset is built by translation. The results show that post-edition is necessary and that the translate-train cross-lingual strategy obtains better results overall, although the gain is lower when tested in a dataset that has been built natively from scratch. Our code and datasets are publicly available under open licenses.", |
| | } |
| | ``` |
| | **APA:** |
| | Heredia, M., Etxaniz, J., Zulaika, M., Saralegi, X., Barnes, J., & Soroa, A. (2024). XNLIeu: a dataset for cross-lingual NLI in Basque. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 4177–4188). Association for Computational Linguistics. |
| | <!-- |
| | ## Dataset Card Contact |
| | [More Information Needed]--> |
