# Post-hoc Utterance Refining Method by Entity Mining
for Faithful Knowledge Grounded Conversations

Yoonna Jang∗, Suhyune Son∗, Jeongwoo Lee∗, Junyoung Son, Yuna Hur,
Jungwoo Lim, Hyeonseok Moon, Kisu Yang, Heuiseok Lim†
Department of Computer Science and Engineering, Korea University
{morelychee, ssh5131, time79779, s0ny, yj72722,
wjddn803, glee889, willow4<EMAIL_ADDRESS>
###### Abstract

Despite the striking advances in recent language generation performance,
model-generated responses have suffered from the chronic problem of
hallucinations that are either untrue or unfaithful to a given source.
Especially in the task of knowledge grounded conversation, the models are
required to generate informative responses, but hallucinated utterances lead
to miscommunication. In particular, entity-level hallucination that causes
critical misinformation and undesirable conversation is one of the major
concerns. To address this issue, we propose a post-hoc refinement method
called REM. It aims to enhance the quality and faithfulness of hallucinated
utterances by refining them based on the source knowledge. If the generated
utterance has a low source-faithfulness score with the given knowledge, REM
mines the key entities in the knowledge and implicitly uses them for refining
the utterances. We verify that our method reduces entity hallucination in the
utterance. Also, we show the adaptability and efficacy of REM with extensive
experiments and generative results. Our code is available at
https://github.com/YOONNAJANG/REM.

00footnotetext: ∗These authors contributed equally to this
work.00footnotetext: †Corresponding author.

##  1 Introduction

The knowledge grounded conversation (KGC; also called knowledge grounded
dialogue, KGD), which is a subfield of the dialogue systems, is a task of
generating human-like utterances by referring to specialized knowledge such as
Wikipedia111https://www.wikipedia.org/ (Zhao et al., 2022; Li et al., 2022c).
The KGC task requires the ability to generate fluent and informative
utterances based on source knowledge. Considering this ability, there has been
numerous concurrent research on the KGC to build models that can have
conversations with human-like expertise (Ma et al., 2020; Yu et al., 2022;
Shuster et al., 2022a; Liu et al., 2022; Sun et al., 2023; Xiao et al., 2023).

A colossal number of training corpora and parameters have brought the recent
explosion of language generation performance in pre-trained language models
(PLMs) (Brown et al., 2020; Raffel et al., 2020) and large language models
(LLMs) (Thoppilan et al., 2022; Touvron et al., 2023; OpenAI, 2023). However,
hallucination, which has been a chronic problem in natural language
generation, is still cited as one of the biggest challenges remaining unsolved
(Ji et al., 2023). In the KGC task, even though the ground truth knowledge is
given, the models make the error of generating hallucinated utterances not
faithful to the source knowledge (Li et al., 2022a; Ji et al., 2023).

Figure 1: An illustrated example of the utterances with entity-level
hallucination (i.e., case 1 and 2) and desirable utterances in knowledge
grounded conversation. In the source-faithful utterance, entities of the given
source knowledge are included, making the utterance informative.

Especially, entity-level hallucination, generating names of entities that are
incorrect or not present in the source document (Nan et al., 2021), causes
critical misinformation and jeopardizes the flow of the conversation (Das et
al., 2023). As shown in Case 1 of Figure 1, the previous KGC models generate
the utterance containing information about the wrong entity, which is not
given in the knowledge. Further, the generated utterance is excessively
general while not considering sufficient entities that align with the context
of the conversation, as shown in Case 2. These deficiencies can undermine the
helpfulness of AI conversational models. Though previous research has tried to
mitigate them in general domain conversation (Shuster et al., 2021), research
to address the entity-level hallucination in KGC remains in dark (Zhang et
al., 2022).

To address these entity-level hallucination problems, we propose a post-hoc
utterance refining method by entity mining, called REM, for more desirable and
source-faithful KGC. REM can be used to refine the unfaithful utterances
generated by previous models in a plug-and-play manner. To refine utterances,
we leverage the entity mining method, which extracts the named entities to
implicitly utilize key information in the knowledge in a multi-tasking manner.
With this simple but effective method, REM aims to mitigate entity-level
hallucination and lead to a more successful conversation.

In order to measure the effectiveness of REM, we conduct an extensive
empirical evaluation. First, to demonstrate the post-hoc refining ability of
REM, we experiment with refining the utterances generated by baseline models
on three KGC datasets. We investigate the flexibility and validity of REM with
cross-data experiments and adversarial data refining experiments,
respectively. In addition, we conduct an ablation study and human evaluation
to verify the effectiveness of our method, showing the improvement of the
source-faithfulness score and entity coverage of refined utterances. We also
demonstrate the scalability of REM by applying it to large language models.
From a case study comparing the refined results of REM and baseline utterance,
we demonstrate that our REM model improves the source-faithfulness in the
utterances.

Our contributions are threefold: (1) We propose a post-hoc refining method by
implicitly mining key entities in the knowledge for more source-faithful
conversation; (2) We show that our simple but effective method is adaptable to
the existing models, including large language models, in a plug-and-play
manner; (3) We substantiate that our method reduces entity-level hallucination
and accomplish more desirable knowledge grounded conversation with diverse
experiments.

##  2 Related Work

###  2.1 Knowledge Grounded Conversation

In the task of knowledge grounded conversation (KGC), the systems aim to
generate an informative conversation based on specialized knowledge. To
support research in this area, publicly available datasets for the KGC task
have been developed (Gopalakrishnan et al., 2019; Moon et al., 2019; Shuster
et al., 2022b). These datasets focus particularly on generating informative
conversations on specific topics (Dinan et al., 2018; Zhou et al., 2018; Jang
et al., 2022). Building upon these KGC datasets, there has been active
research to generate contextually consistent utterances while utilizing the
source knowledge. Kim et al. (2020); Adolphs et al. (2021); Wang et al.
(2021); Zhang et al. (2023); Feng et al. (2023) incorporate knowledge and
context selectors to filter out irrelevant knowledge sentences and redundant
dialogue history, respectively. Additionally, (Niu et al., 2023) propose the
history-adapted knowledge copy (HAKC) network, which selectively chooses
context-aware knowledge to maintain dialogue coherence. Likewise, diverse
research is widely conducted in KGC tasks.

###  2.2 Knowledge Hallucination in KGC

Despite the remarkable advancements, KGC systems are known to suffer from
knowledge hallucination generating unfaithful utterances to source knowledge
(Dziri et al., 2021b). To address hallucination, Shuster et al. (2021) propose
neural-retrieval-in-the-loop architectures to improve knowledge-ability
consisting of retrievers, rankers, and encoder-decoders. Additionally, Li et
al. (2022b) proposes a method that utilizes entity and relation information
from a knowledge graph to generate more faithful utterances. However, in KGC,
there is a lack of studies on entity-level hallucination, which directly
identifies and controls the generation of unfaithful utterances (Das et al.,
2023). Although Dziri et al. (2021a) explore the study on entity-level
hallucination, this study only considers cases where the source of knowledge
is a knowledge graph. Therefore, there is a need for research on entity-level
hallucination in KGC tasks where the source knowledge is provided.

###  2.3 Utterance Refining Methods

Refining methods have been studied to improve the generation results when they
are not satisfactory or not in the desired intention. (He, 2021; Geng et al.,
2021; Sun et al., 2022; Bao and Zhang, 2023) Especially in dialogue systems,
the researches aim to improve the quality of response by applying the refining
method have been proposed (Tran and Nguyen, 2018; Posokhov et al., 2022; Wang
et al., 2022). To tackle the limitation of generating uninformative or not
engaging utterances (Shen et al., 2018), Weston et al. (2018) retrieves the
utterance and refines it by regenerating the retrieved utterance. Moreover,
for fascinating dialogue systems, the ability to generate personalized
responses according to the user’s dialogue history is required (Cao et al.,
2022). However, as history is usually long and noisy, the problem of missing
personalized information exists (Kasahara et al., 2022). To address this
problem, Zhong et al. (2022) refines the user dialogue history to extract
valuable information. Furthermore, they generate personalized dialogue by
utilizing refined history information. Also, Song et al. (2020) adopts a
rewriting method for persona-consistent dialogue generation. In this study,
our goal is to reduce entity-level hallucination, which is critical in
informative conversation (Corbelle et al., 2022), with the refining method.

##  3 Proposed Method

Our proposed REM aims to refine the model-generated utterance to be more
faithful to the source knowledge, as shown in Figure 3. The utterance is
filtered by the faithfulness scoring function, whether to be refined or not.
We train the pre-trained language model with an encoder-decoder structure in
the REM method. In the REM model, the entity miner extracts the named entities
from the source knowledge and learns them implicitly. The utterance generator
makes more faithful utterances based on the key information extracted by the
entity miner. In this section, we formulate a KGC task and our proposed
utterance refining method, and describe the REM model and its training
process.

(a)

(b)

Figure 2: The graphical model of (a) the knowledge grounded conversation (KGC)
and (b) our proposed REM. The black line is modeled by the KGC model parameter
$\theta$. The yellow lines represent the entity miner, which is modeled
implicitly, and the blue lines indicates the utterance generator,
respectively. The colored lines are modeled by the REM parameter $\psi$.

###  3.1 Knowledge Grounded Conversation

As depicted in Figure 2 (a), the KGC models generate the informative utterance
$\boldsymbol{\hat{u}}$ considering both the conversational context (or
dialogue history) $\boldsymbol{c}$ and the corresponding source knowledge
$\boldsymbol{k}$ as follows:

$\begin{split}p_{\theta}(\boldsymbol{\hat{u}}|\boldsymbol{k},\boldsymbol{c})=\prod_{t=1}^{n}{p_{\theta}(\hat{u}_{t}|\hat{u}_{<t},\boldsymbol{k},\boldsymbol{c})},\end{split}$
(1)

where $n$ indicates the max sequence length of the utterance, and $\theta$
denotes the KGC model parameter.

Figure 3: The architecture of REM model, which consists of entity miner and
utterance generator. The utterance $\boldsymbol{\hat{u}}$ with a lower source-
faithfulness score than threshold $\tau$ enters as input to REM with source
knowledge $k$. The entity miner extracts entities $e$ from $k$, in white
circles, while the utterance generator refines the $\boldsymbol{\hat{u}}$ with
the extracted entity $e$.

###  3.2 Post-hoc Utterance Refining

While KGC models aim to generate informative utterances, model-generated
utterance $\boldsymbol{\hat{u}}$ may not reflect the input source knowledge
faithfully, as shown in Figure 1. In this context, we first filter out the
utterance which is not faithful to the source. Then we renovate the given
utterance into the refined utterance $\boldsymbol{\tilde{u}}$ which represents
the intended knowledge better by utilizing the model-generated utterance
$\boldsymbol{\hat{u}}$ and its corresponding knowledge $\boldsymbol{k}$ from
KGC models as follows:

$p_{\psi}(\boldsymbol{\tilde{u}}|\boldsymbol{k},\boldsymbol{\hat{u}})=\prod_{t=1}^{n}{p_{\psi}(\tilde{u}_{t}|\tilde{u}_{<t},\boldsymbol{k},\boldsymbol{\hat{u}})},\\\
$ (2)

where $\psi$ denotes the REM model parameter.

#### Faithfulness Filtering

To filter the model-generated utterance $\boldsymbol{\hat{u}}$, we quantify
the source-faithfulness score, which indicates how $\boldsymbol{\hat{u}}$ is
consistent with the source knowledge $\boldsymbol{k}$. To this end, we adopt
the DAE (Goyal and Durrett, 2020) as a scoring function to estimate the
entailment between the source knowledge and $\boldsymbol{\hat{u}}$ considering
the dependency arc-level consistency of the utterance.

For efficiency, we only refine the utterances that have lower scores than the
threshold according to the source-faithfulness score, as follows:

$\boldsymbol{\tilde{u}}=\begin{cases}\mathrm{REM}(\boldsymbol{k},\boldsymbol{\hat{u}})&\text{:
if $\text{{score}}(\boldsymbol{\boldsymbol{k},\hat{u}})$ < $\tau$}\\\
\boldsymbol{\hat{u}}&\text{: otherwise},\\\ \end{cases}$ (3)

where $\text{{score}}(\cdot)$ denotes the scoring function (i.e., DAE) and
$\tau$ is a threshold value between 0 and 1. When $\tau$ is 1, all utterances
are re-generated as $\boldsymbol{\tilde{u}}$; otherwise, when $\tau$ is 0, all
utterances are not refined. We utilize this filtering module only in the
inference step, not in the training step.

#### Refining Utterance by Entity Mining (REM)

Our refining method, REM, is decomposed into two sub-modules that are (i)
entity miner and (ii) utterance generator, as shown in Figure 2. The refining
task is formulated as follows:

$p_{\psi}(\boldsymbol{\tilde{u}}|\boldsymbol{k},\boldsymbol{\hat{u}})\propto\underbrace{p_{\psi}(\boldsymbol{e}|\boldsymbol{k},\boldsymbol{\hat{u}})}_{\text{Entity
miner}}\underbrace{p_{\psi}(\boldsymbol{\tilde{u}}|\boldsymbol{e},\boldsymbol{k},\boldsymbol{\hat{u}})}_{\text{Utterance
generator}}$ (4)

These two modules are trained in a multi-tasking manner with parameter $\psi$.
The former mines entities $\boldsymbol{e}$ from source knowledge and learns
entity-level knowledge implicitly. The latter learns to generate the utterance
$\boldsymbol{u}$ with the implicitly mined entities, which is key information
of source knowledge, reducing entity-level hallucinations and producing more
source-faithful utterances.

###  3.3 Training Objectives

The training objective of REM mainly falls into named entity recognition (NER)
by entity miner and utterance re-generation (GEN) by utterance generator.

To predict the named entities, the NER head is attached on top of the encoder
and mines the entities inside the knowledge $\boldsymbol{k}$. It makes the
model learn the essential entity information, enhancing the interpretation of
the source knowledge. To train NER, we tag named entities with one of the
following entity types {‘LOC’,‘PER’,‘ORG’, ‘MISC’} using the off-the-shelf
entity tagging module222https://github.com/flairNLP/flair for the source
knowledge in the data.

The NER loss $\mathcal{L}_{NER}$ is only defined for the NER label $l_{i}$
tagged for each token $k_{i}$ in $\boldsymbol{k}$ and minimized during
training as follows:

$\mathcal{L}_{NER}=-\cfrac{1}{N_{k}}\displaystyle\sum_{i=1}^{N_{k}}l_{i}\log
p(k_{i}|\boldsymbol{\hat{u}}),$ (5)

where $N_{k}$ denotes the token length of the source knowledge.

The language modeling head (GEN) learns to refine the given model-generated
utterance $\boldsymbol{\hat{u}}$ along with $\boldsymbol{k}$ in an auto-
regressive manner while extracting entities $\boldsymbol{e}$. The model learns
to compose the information of the inputs and extracted entities from the
encoder hidden states. It is trained by minimizing the following loss
function:

$\mathcal{L}_{GEN}=-\cfrac{1}{N_{u}}\displaystyle\sum_{i=1}^{N_{u}}\log
p(u_{i}|u_{<i},\boldsymbol{\boldsymbol{k},\hat{u}};\boldsymbol{e}),$ (6)

where $\boldsymbol{e}$ is implicitly mined in the encoder during training.

The final training objective for a REM trained on both tasks is the following,
where $\lambda_{n}$ is the training hyperparameter:

$\mathcal{L}_{REM}=\lambda_{1}\mathcal{L}_{NER}+\lambda_{2}\mathcal{L}_{GEN}$
(7)

##  4 Experimental Settings

In this section, we introduce the dataset, the automatic evaluation metric,
and the baseline models used in the experiments. The implementation details
are attached in Appendix C.

###  4.1 Datasets

We utilize three datasets as our testbed, but we only use instances for
training and testing where ground truth knowledge is given in the data.

FoCus (Jang et al., 2022), which considers both knowledge and persona in
conversation, has been released. In FoCus, machine generates utterances with
customized and knowledgeable utterances about world landmarks. Wizard of
Wikipedia (WoW) (Dinan et al., 2018), which is a widely used benchmark,
consists of Wizard and Apprentice talking to each other on various topics in
Wikipedia. Another dataset, CMUDoG (Zhou et al., 2018) includes conversations
between two speakers discussing different aspects of a specific movie, such as
information, plot, etc. The data statistics are presented in Appendix A.

###  4.2 Automated Evaluation Metrics

We evaluate the refining performance of REM with automated metrics in three
criteria: source-faithfulness, reference matching, and diversity. To evaluate
source-faithfulness, we adopt DAE, entity coverage (EC), and entity type
coverage (TC). DAE (Goyal and Durrett, 2020) is used to assess whether the
system accurately reflects the facts from the given knowledge when generating
utterances at the level of dependency arcs. Furthermore, we utilize two
entity-level metrics (EC and TC), following subsequent paragraphs, to evaluate
the source-faithfulness in terms of the entity. For the reference matching
n-gram score, we utilize chrF (Popović, 2015), ROUGE-L (Lin, 2004) and
SacreBLEU (Post, 2018) to evaluate how close the generated hypothesis is to
the ground-truth answer in the test set. For diversity evaluation,
Distinct-N333https://github.com/neural-dialogue-metrics/Distinct-N (Li et al.,
2015) is adopted.

#### Entity Coverage and Entity Type Coverage

We compute the entity coverage (EC) and entity type coverage (TC) to quantify
the extent of correct entities and context coherence, respectively. To compute
EC and TC, we extract the named entities from the generated (model-predicted)
utterance $\boldsymbol{u_{p}}$ (including $\boldsymbol{\tilde{u}}$ and
$\boldsymbol{\hat{u}}$), source knowledge $\boldsymbol{k}$, and ground truth
utterance $\boldsymbol{u}$, respectively, using the off-the-shelf NER model
(Schweter and Akbik, 2020) (The distribution of named entity types are
described in Appendix B). First, we evaluate the ratio of the named entities
residing in the generated utterance $\boldsymbol{u_{p}}$ by comparing it with
the ground truth utterance $\boldsymbol{u}$ as follows:

$EC=\frac{{\mathcal{N}\big{(}(E_{\boldsymbol{k}}\cap E_{\boldsymbol{u}})\cap
E_{\boldsymbol{u_{p}}}\big{)}}}{\mathcal{N}\big{(}E_{\boldsymbol{k}}\cap
E_{\boldsymbol{u}}\big{)}},$ (8)

where $\mathcal{N}(l)$ is the number of values in list $l$ and
$E_{\boldsymbol{k}}$ is the set of named entities in source knowledge.
$E_{\boldsymbol{u}}$ and $E_{\boldsymbol{u_{p}}}$ indicate the set of named
entities in ground truth utterance and generated utterance, respectively.

In addition, we evaluate TC, as existing models produce fluent text but
demonstrate low context coherence. To identify the intent of context and the
information that should be included in the response, we leverage the named
entity types in the ground truth utterance $\boldsymbol{u}$. For example, if
the entity type ‘LOC’ has the highest proportion among the named entities in
$\boldsymbol{u}$, it indicates that the conversation is focused on location-
related information. In such cases, the model should generate responses that
are relevant to the location to ensure coherence and relevance in the
conversation.

To this end, we compute the ratio of name entity types extracted from the
generated utterance $\boldsymbol{u_{p}}$ compared to the ground truth
utterance $\boldsymbol{u}$ in each named entity type.

$TC=\frac{1}{|T|}\sum_{t}^{T}\Big{(}\frac{\mathcal{N}\big{(}E^{t}_{\boldsymbol{u_{p}}}\big{)}}{\mathcal{N}\big{(}E^{t}_{\boldsymbol{u}}\big{)}}\Big{)},$
(9)

where $T$ is the set of named entity types {‘LOC’, ‘PER’, ‘ORG’, ‘MISC’}.

###  4.3 KGC Baselines

#### BART

BART (Lewis et al., 2020a) is Transformer-based (Vaswani et al., 2017) pre-
trained model with the encoder-decoder structure. It utilizes various noising
methods and in-filling schemes during pre-training. We fine-tune BART on three
KGC datasets and use its predicted utterance for train, validation, and test
set.

#### INFO

INFO (Lim et al., 2022) leverages RAG (Lewis et al., 2020b) to enhance the
factuality of the generation results in FoCus dataset. It retrieves a large
number of knowledge documents and generates informative conversations based on
them.

#### EDMem

We utilize and refine the utterances generated by EDMem (Zhang et al., 2022).
EDMem is pre-trained on Wikipedia documents to learn entity embeddings and
incorporates entity knowledge for entity-intensive dialogue generation.

#### ITDD

ITDD (Li et al., 2019) is the best-performing method on the CMUDoG dataset.
With a two-pass decoder inspired by the human cognitive process, it improves
context coherence and knowledge correctness.

|  | Source-faithfulness | Reference Matching | Diversity
---|---|---|---|---
Data | Model | EC (%) | TC (%) | DAE | chrF | ROUGE-L | BLEU | Dist-1 | Dist-2
| BARTbase |  7.54 | 13.85 | 0.60 | 19.47 | 28.04 |  2.51 | 0.37 | 0.80
FoCus | \+ REMbase (Ours) | 23.40 | 20.32 | 0.83 | 30.38 | 33.14 |  9.47 | 0.28 | 0.73
2-10[0.8pt/1.2pt] | BARTbase | 14.23 |  8.80 | 0.84 | 31.70 | 34.02 | 12.72 | 0.31 | 0.78
WoW | \+ REMbase (Ours) | 14.57 |  9.03 | 0.87 | 32.44 | 34.17 | 12.83 | 0.29 | 0.77
2-10[0.8pt/1.2pt] | BARTbase |  3.40 |  3.90 | 0.26 | 12.92 | 13.14 |  3.25 | 0.40 | 0.83
CMUDoG | \+ REMbase (Ours) |  4.95 |  7.55 | 0.40 | 15.06 | 11.92 |  2.49 | 0.25 | 0.70
| INFO |  0.05 | 24.40 | 0.79 | 50.35 | 54.27 | 32.55 | 0.26 | 0.70
FoCus† | \+ REMlarge (Ours) |  0.11 | 25.48 | 0.86 | 49.98 | 52.30 | 30.17 | 0.24 | 0.68
2-10[0.8pt/1.2pt] | EDMem |  0.00 |  3.50 | 0.50 | 14.30 | 15.21 |  2.90 | 0.28 | 0.71
WoW† | \+ REMlarge (Ours) |  4.51 |  6.51 | 0.77 | 19.46 | 18.30 |  4.25 | 0.25 | 0.68
2-10[0.8pt/1.2pt] | ITDD |  0.00 |  1.42 | 0.12 |  9.30 |  9.39 |  1.71 | 0.48 | 0.82
CMUDoG† | \+ REMlarge (Ours) |  1.01 |  3.96 | 0.30 | 13.64 | 10.44 |  1.74 | 0.24 | 0.67

Table 1: Experimental results of baseline utterance refining with REM. The top
half shows the results of refining (1) the fine-tuned baselines, and the
bottom half shows the results of refining (2) the existing baselines. Numbers
in boldface indicate higher scores. † denotes the evaluation settings of the
existing models, and the vanilla setting (without †) denotes the evaluation
settings of our fine-tuned baseline. The utterances are filtered with
$\tau=0.5$.

##  5 Results and Analysis

In this section, we report the experimental results and analysis. We first
discuss the post-hoc refining ability of our method in section 5.1. Then, we
evaluate the flexibility of REM through cross-data refining experiments in
section 5.2. We also conduct experiments on refining adversarial utterances in
section 5.3, and perform an ablation study in section 5.4. Furthermore, we
provide the results of human evaluation in section 5.5, and present a case
study in section 5.7. Finally, we analyze the application of REM to large
language models in section 5.6.

###  5.1 Post-hoc Refining Ability

To demonstrate the post-hoc refining ability, we evaluate the refining
performance of REM with vanilla PLM fine-tuned on three datasets and existing
models from previous studies. The results are in Table 1 where REMbase and
REMlarge refer to the models trained using the REM method on the BART-base and
BART-large (Lewis et al., 2020a).

#### Fine-tuned Baselines

The comparison results of REM with the vanilla PLM fine-tuned on three
datasets are presented at the top of Table 1. We fine-tune the BART-base model
with each dataset and refer to it as BARTbase. To the generated outputs of
BARTbase, we evaluate the refining performance with REMbase. The results
demonstrate that the REM leads to improvements not only in the scores of
source-faithfulness metrics but also in the reference matching metrics. We
also show the ability of REM that refines the utterances of the larger model
in Appendix F.

In contrast, REM shows a tendency to decrease the performance of diversity
metrics. This indicates that in BARTbase-generated utterances, there are
tokens that contribute to increased diversity but also lead to hallucination.
These hallucinated tokens are subsequently refined through the REM process. In
particular, the significant improvements in EC and TC metrics suggest that
entities that cause entity-level hallucination are refined during the refining
process with REM.

#### Existing Baselines

To investigate the adaptability of REM, we compare the refining performance
with previous studies, and the results are annotated with † symbol in Table 1.
We re-implement EDMem, ITDD, and INFO for fair comparison with REMlarge. The
results reveal that REMlarge exhibits an improvement in source-faithfulness
performance. Additionally, it demonstrates enhanced generation performance in
the reference matching score, excluding INFO. The reason is that INFO utilizes
a large number of knowledge documents for generation, whereas REM generates
utterances based on the given ground truth knowledge within the dataset.
Furthermore, while the source-faithfulness score increases, the diversity
score decreases. This result discusses that REM removes tokens that enhance
diversity but also contribute to entity hallucination, similar to the results
observed with vanilla PLM.

|  | Source-faithfulness | Ref. Matching
---|---|---|---
Train | Test | EC (%) | TC (%) | DAE | chrF | R-L
FoCus |  | 50.64 | 29.88 | 0.84 | 50.47 | 46.55
3-7[1.2pt/1.6pt] WoW |  |  0.03 |  8.35 | 0.03 |  9.24 |  8.65
CMUDoG | FoCus | 17.02 | 16.20 | 0.29 | 21.32 | 15.72
WoW |  | 20.84 | 13.59 | 0.77 | 37.18 | 31.55
3-7[1.2pt/1.6pt] FoCus |  | 21.21 | 14.51 | 0.78 | 36.76 | 30.12
CMUDoG | WoW | 18.99 | 12.73 | 0.74 | 35.23 | 29.16
CMUDoG |  |  6.90 |  9.50 | 0.43 | 16.42 | 12.44
3-7[1.2pt/1.6pt] FoCus |  | 12.51 | 29.17 | 0.83 | 13.37 |  7.08
WoW | CMUDoG |  3.40 |  3.90 | 0.26 | 12.92 | 13.14

Table 2: The result of cross-data experiments with REMlarge. All utterances
are refined with $\tau=1.0$.

###  5.2 Cross-data Utterance Refining

To inspect the flexibility of REM, we conduct cross-data experiments, and the
results are shown in Table 2.

REM trained on CMUDoG consistently underperforms in source-faithfulness and
reference matching scores on the other two datasets. When refining FoCus test
set with REM trained on CMUDoG, the performance significantly decreases in all
metrics. This tendency is similarly shown in the results of WoW test set.
Likewise, the REM trained on WoW exhibits a decrease in performance on FoCus
and CMUDoG test sets.

According to the previous comprehensive human study Dziri et al. (2022b) that
analyzes the portion of hallucination in KGC dataset, the reason for the
performance decrease is hallucinated utterances in WoW and CMUDoG datasets.
The results revealed that only 24.15% of utterances in wow and 16.2% in CMUDoG
exhibited entailment. These proportions indicate that a significant portion of
utterances contains knowledge hallucination. Consequently, hallucination in
the dataset has an impact on model training and evaluation.

On the other hand, the model trained on FoCus dataset demonstrates high
source-faithfulness performance across all datasets. We assume that the reason
for performance differences lies in dataset construction. While WoW and CMUDoG
contain utterances that provide responses without knowledge, FoCus ensures
that all utterances are generated based on knowledge in the dataset.
Therefore, the REM trained on FoCus has facilitated the training of a more
faithful KGC model.

|  | Source-faithfulness | Ref. Matching
---|---|---|---
Data | Model | EC (%) | TC (%) | DAE | chrF | R-L
| ADV | 27.77 | 19.28 | 0.63 | 27.09 | 31.10
2-7[1.2pt/1.6pt] | \+ REMbase | 46.68 | 35.41 | 0.85 | 42.49 | 37.00
FoCus | \+ REMlarge | 48.04 | 34.19 | 0.87 | 41.91 | 36.87
| ADV |  5.94 |  5.82 | 0.32 | 16.53 | 16.78
2-7[1.2pt/1.6pt] | \+ REMbase | 18.93 | 12.71 | 0.75 | 33.03 | 29.33
WoW | \+ REMlarge | 17.58 | 12.38 | 0.75 | 34.09 | 28.74
| ADV |  3.33 |  4.98 | 0.23 | 12.07 | 12.04
2-7[1.2pt/1.6pt] | \+ REMbase |  5.21 | 10.08 | 0.44 | 15.37 | 10.92
CMUDoG | \+ REMlarge |  5.99 |  9.24 | 0.42 | 15.55 | 11.43

Table 3: Experimental results of adversarial data refining. ‘ADV’ denotes the
adversarially edited utterances by ChatGPT. All utterances are refined with
$\tau=1.0$.

###  5.3 Adversarial Data Refining

To investigate the validity of REM, we have prompted ChatGPT OpenAI-Blog
(2022) to generate the synthetic data by adversarially changing the ground
truth utterance444The prompts are shown in Appendix E.. We demonstrate the
performance of REM by refining the utterances that are distorted to be
unfaithful to the source knowledge; all data are refined with ($\tau=1.0$). As
presented in Table 3, compared to the adversarially synthesized data (ADV),
both source-faithfulness and reference matching scores increased significantly
after refining. ROUGE-L score of the ADV is slightly higher in CMUDoG, which
is likely due to the characteristic that the utterances of CMUDoG have much
shorter lengths than other datasets Dziri et al. (2022a).

###  5.4 Ablation Study

To explore the effect of entity miner, we conduct an ablation study of our
proposed method. In Table 4, we present the results on the FoCus dataset,
which has the most informative conversations in section 5.2. We compare the
REM model trained with multi-task learning and only trained with utterance
refining (w/o NER).

In case the model is trained without entity miner, the results show a
performance decrease across the board except in one case. Our proposed REM
method, which learns essential information with entity miner, can be
demonstrated to be effective for source-faithfulness and reference matching
scores. The DAE score of the large models shows better performance without
entity miner. The large model shows unstable results in section 5.3. However,
a significant improvement in the performance of the REM model is shown, with
or without entity miner, compared to the baseline utterance. This suggests
that refining helps increase the quality and source-faithfulness of fatal
utterances.

| Source-faithfulness | Ref. Matching
---|---|---
Model | EC (%) | TC (%) | DAE | chrF | R-L
BARTbase | 19.47 | 28.04 |  7.54 | 13.85 | 0.60
2-6[1.2pt/1.6pt] \+ REMbase wo NER | 30.24 | 33.08 | 23.28 | 20.22 | 0.82
\+ REMbase | 30.38 | 33.14 | 23.40 | 20.32 | 0.83
2-6[1.2pt/1.6pt] \+ REMlarge wo NER | 31.30 | 34.44 | 24.17 | 20.26 | 0.82
\+ REMlarge | 31.52 | 34.56 | 20.58 | 20.58 | 0.84

Table 4: The result of ablation study on training objectives. The utterances
are filtered with $\tau=0.5$ Figure 4: Results of human evaluation. F, S and
P, attached after the dataset name, denote fluency, source faithfulness and
paraphrasing, respectively.

###  5.5 Human Evaluation

To qualitatively evaluate the results before and after refining, we perform a
human evaluation on 100 randomly sampled utterance examples, each from three
datasets. One example contains an utterance produced by the baseline and the
refined utterance by REM, and we evaluate them with three criteria: 1)
fluency, 2) source-faithfulness, and 3) paraphrasing. The first criterion
assesses naturalness by measuring the fluency of the generated results. The
second criterion measures source-faithfulness by assessing whether the
sentence is factually consistent with the given knowledge. In addition, the
third criterion evaluates whether the given knowledge has been appropriately
reorganized and incorporated into the utterance rather than just copied and
pasted. We provide the questionnaire used in Appendix G

The results of the comparative human evaluation of baseline utterances with
scores below the threshold and utterances after refining are shown in Figure 4
($\tau=0.5$). Across all data and criteria, the refined utterances win in most
cases. Especially in FoCus, refined utterances do not lose to the utterance
before refining. REM performs the worst in CMUDoG, which is similar to the
experiment in Section 5.1. The dissimilarity between the CMUDoG and the other
two datasets can be attributed to its predominant resemblance to a general
utterance instead of the knowledge grounded conversations. Nevertheless, we
qualitatively demonstrate the effectiveness of REM with a significantly higher
number of winning cases.

###  5.6 REM-LLM

|  | Source-faithfulness | Ref. Matching
---|---|---|---
Data | Model | EC | TC | DAE | chrF | R-L
| BARTbase |  7.56 | 13.85 | 0.60 | 19.47 | 28.03
2-7[1.2pt/1.6pt] | \+ LLM | 23.54 | 20.14 | 0.86 | 26.96 | 33.57
FoCus | \+ LLMREM | 25.28 | 22.18 | 0.89 | 28.27 | 33.66
| BARTbase | 14.23 |  8.79 | 0.84 | 31.73 | 34.02
2-7[1.2pt/1.6pt] | \+ LLM | 14.34 |  8.89 | 0.85 | 31.95 | 33.87
WoW | \+ LLMREM | 14.57 |  9.12 | 0.86 | 32.27 | 33.84
| BARTbase |  3.40 |  3.90 | 0.26 | 12.92 | 13.14
2-7[1.2pt/1.6pt] | \+ LLM |  4.93 |  6.07 | 0.36 | 13.26 | 12.43
CMUDoG | \+ LLMREM |  7.36 | 11.39 | 0.57 | 14.70 | 11.23
| LLMKGC | 42.74 | 26.72 | 0.84 | 38.16 | 40.64
2-7[1.2pt/1.6pt] | \+ LLM | 44.95 | 27.45 | 0.89 | 39.61 | 39.61
FoCus | \+ LLMREM | 45.83 | 28.46 | 0.91 | 40.01 | 41.98
| LLMKGC |  6.91 |  5.87 | 0.36 | 18.68 | 18.65
2-7[1.2pt/1.6pt] | \+ LLM |  9.27 |  6.73 | 0.47 | 21.24 | 20.17
WoW | \+ LLMREM | 13.28 |  9.25 | 0.63 | 25.17 | 21.19
| LLMKGC |  4.77 |  7.24 | 0.29 | 14.36 | 13.44
2-7[1.2pt/1.6pt] | \+ LLM |  5.61 |  8.53 | 0.38 | 14.36 | 12.21
CMUDoG | \+ LLMREM |  7.88 | 14.26 | 0.54 | 14.36 | 10.04

Table 5: The result of REM with the large language model (denoted with +)
refining the KGC utterances of BARTbase and large language model (LLMKGC). The
utterances are filtered with $\tau$ = 0.5

To show the scalability of our method to large language models (LLMs), we
apply REM method to the prompt of not-tunable LLM (i.e., ChatGPT) beyond fine-
tuning the models. In this experiment, large language models with (LLMREM) and
without (LLM) the REM method refine the utterance of the KGC baseline model
(BARTbase) and ChatGPT (LLMKGC). LLM is required to refine the utterance
considering the given source knowledge, while LLMREM is made to modify the
utterance by extracting key entities from the knowledge and utilizing them. We
attach the prompts used in Appendix H.

As shown in Table 5, both LLM and LLMREM show improved source-faithfulness
performance, especially in CMUDoG and FoCus. LLMREM is able to achieve the
most significant improvement by entity mining and refining the utterance with
key entities. This demonstrates that when prompting with LLMs, more source-
faithful utterances can be produced with REM by asking LLMs to find the
important parts and use them for making responses.

FoCus
---
[Knowledge]
Designed by French architect Paul Andreu, the NCPA opened in 2007 and is the
largest theatre complex in Asia.
[Persona Knowledge]
I love art. I’m interested in architecture. I love lakes. I want to go on a
trip. I want to visit China.
[Utterance]
BARTbase : National for Per Arts in opened 2007.
\+  REMbase : National Center for the Performing Arts (NCPA) opened in 2007
and is the largest theatre complex in Asia. If you love art, you have to come
see it with your own eyes!

WoW
---
[Knowledge]
Among other honors, he has won nine Goyas, two European Film Awards and an
Oscar.
[Utterance]
BARTbase : I’ve seen it a few times. It was directed by Alejandro Amená
\+  REMbase : of course! He has won nine Goyas, two European Film Awards, and
an Oscar! That’s a lot of awards for a director!

CMUDoG
---
[Knowledge]
The Asgardian Loki encounters the Other, the leader of an extraterrestrial
race known as the Chitauri. …, Loki takes the Tesseract and uses his scepter
to enslave Selvig and a couple of other agents, including Clint Barton, to aid
him in his getaway.
[Utterance]
BARTbase : you should watch the avengers 2 and 3
\+  REMbase : you should watch the avengers 2 and 3, they are really good as
well. what did you think of the scene where Loki takes the Tesseract?

Table 6: Examples of refined utterances by REM on three datasets.

###  5.7 Case Study

To analyze the generative results of REM, we present the examples of three
datasets in Table 6. In FoCus example, the utterance of baseline answers only
provides the information of year, but with the imperfect entity mention ‘Per’.
However, after being refined with REM, the utterance provides more specific
knowledge with correct entity mentions, even utilizing the persona knowledge.
In WoW example, the baseline utterance provides knowledge that is not given in
the knowledge, but REM gives source-faithful information. In CMUDoG example,
REM provides more detailed factual knowledge in the utterance than in the
baseline utterance. We show more examples also in Appendix I.

##  6 Conclusion

In this work, we proposed REM, a post-hoc refining method for improving the
source-faithfulness of the utterances in the knowledge grounded conversation
(KGC). REM enabled simple but effective refining by extracting entities from
the source knowledge given in the KGC task. It makes the model learn the key
information implicitly and use it for refining the utterance more faithful to
the source knowledge. We presented extensive experiments applying REM in a
plug-in-play method to various model-generated outputs, showing increased
source-faithfulness and entity coverage after refining. Also, qualitative
analysis and human evaluation proved the refining efficacy of REM. We verified
that our proposed method could be utilized with the prompt of the large
language models.

## Limitations

Our research proposed a method that aims to refine the non-source-faithful
utterances in the knowledge grounded conversation. Though we address the
problem of models not reflecting even the ground truth knowledge given, it is
difficult to solve if the retriever does not give accurate knowledge from the
in-the-wild setting. Therefore, the performance of the refiner may depend on
the retriever’s performance. As it is beyond the scope of this paper, we leave
it as future work. When filtering the utterances by source-faithfulness score,
we only utilized DAE, but other scores or classifiers Dou et al. (2022);
Manakul et al. (2023) can be adopted for filtering according to the data or
domain. There is an area of research in detecting critical errors in generated
results in neural machine translation. Similarly, in the dialogue tasks, the
better the classifier performs in detecting hallucinated utterances, the
better the performance of the refiner will be. In addition, we expect that
existing baseline models could be further improved if trained with REM in an
end-to-end manner, so we leave it as future work.

## Ethics Statement

The datasets used in our work are from previously published papers, so we do
not attach privacy or ethical issues to the dataset. At inference, we will
make the model not generate tokens in a list of several stopwords utilizing
Hatebase555http://hatebase.org, Silva et al. (2016), etc., to avoid generating
harmful utterances that the model may have learned during pre-training. As we
are aware that excessive computational energy used to train models stimulates
environmental problems, we adopt the multi-task learning method for training
entity miner and utterance generator instead of having a separate entity miner
model to improve efficiency. Also, we distribute all codes and model
checkpoints so they do not have to be trained again. We believe that our
refining method can contribute to mitigating the entity-level hallucination of
model-generated utterances and preventing the misuse of AI systems.

## Acknowledgements

This research was supported by the MSIT(Ministry of Science and ICT), Korea,
under the ITRC(Information Technology Research Center) support
program(IITP-2023-2018-0-01405) supervised by the IITP(Institute for
Information & Communications Technology Planning & Evaluation). This work was
supported by Institute of Information & communications Technology Planning &
Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-00368,
A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques).
Also, this work was supported by NCSOFT NLP Center.

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##  Appendix A Dataset Details

FoCus (Jang et al., 2022) is a dataset where a human and a machine take turns
having a conversation about a specific landmark. This dataset consists of
14,452 conversations, with 173,424 utterances. We use the validation set,
which has 1,445 conversations and 17,340 utterances, for the experiment, as
the official test set does not provide ground truth knowledge and utterances.

Wizard of Wikipedia (WoW) (Dinan et al., 2018) is a conversational dataset
based on Wikipedia articles on various topics. The dataset covers 1,365 topics
and consists of 22,311 conversations with a total of 201,999 utterances, which
are divided into 166,787 for train, 17,715 for validation, and 17,497 for
test. We used a random split version of the validation and test set. Only for
evaluating EDMem (Zhang et al., 2022), we follow its test setting, KILT
(Petroni et al., 2021), a variant of the WoW dataset.

CMUDoG (Zhou et al., 2018) is a knowledge grounded conversation dataset with
two speakers conversing based on movie Wikipedia articles. Unlike the WoW and
FoCus, where only one of the two speakers has access to the knowledge, both
speakers of this dataset have access to the content of the article. It has a
resemblance to a generic dialogue compared to the other two datasets. It is
composed of a total of 4,112 conversations with an average of 21.43 turns.

##  Appendix B Distribution of Named Entity-Type

We show the entity type distribution of all datasets, the train, development,
and test sets used for fine-tuning the baseline model BARTbase, in Figure 5.
WoW has the evenest distribution, and FoCus has the second most even
distribution. CMUDoG has the most “PER” entity class among the other three
classes.

Figure 5: Entity type distribution of three datasets.

##  Appendix C Implementation Details

When implementing REM, we adopt BART Lewis et al. (2020a), which has an
encoder-decoder structure, and train the entity mining and utterance
generation tasks. We experiment with BARTbase of 140M parameters and BARTlarge
of 406M parameters. We train REM on the pairs of model-predicted utterances,
generated by the fine-tuned BART baseline, and reference utterance. We
implement the models by exploiting Pytorch Paszke et al. (2019) and
HuggingFace Wolf et al. (2019) with a fixed seed for reimplementation. The
models are trained with a learning rate of 6.25e-5 for 10 epochs with early
stopping. AdamW (Loshchilov and Hutter, 2017) is used as the optimizer. We set
the train batch size to 8 with a gradient accumulation of 32. The time
required for training is about 2 hours per epoch on a single RTX-6000 GPU. For
decoding, we used a beam size of 5, min length of 32, max length of 512, top
$k$ of 50, and no-repeat n-gram size of 2. NER loss weight $\lambda_{1}$ and
GEN loss weights $\lambda_{2}$ are set as 0.5:1, 0.3:1, and 0.7:1 for WoW,
CMUDoG, and FoCus, respectively, according to the preliminary study. We will
open the source codes which we used for the experiments after the review
process.

Data | Model | ACC (NER) | F1 (ACC) | PPL (GEN)
---|---|---|---|---
FoCus | \+ REMbase | 0.991 | 0.944 | 3.778
FoCus | \+ REMlarge | 0.989 | 0.925 | 3.323
WoW | \+ REMbase | 0.998 | 0.938 | 12.742
WoW | \+ REMlarge | 0.997 | 0.913 | 10.788
CMUDoG | \+ REMbase | 1.000 | 1.000 | 29.775
CMUDoG | \+ REMlarge | 0.999 | 0.991 | 27.247

Table 7: The result on the validation set used for training and evaluating the
models with both tasks, NER and GEN. Larger models show slightly higher scores
in GEN, but lower scores in ACC.

##  Appendix D Threshold

To utilize the off-the-shelf metric as our scoring function for filtering, we
manually find the threshold both for effectiveness and efficiency. The high
threshold is intended for evaluating the source-faithfulness of a given
utterance strictly. A low threshold, on the other hand, targets only those
utterances that are not faithful to the knowledge and aims to regenerate only
fatal cases. As shown in Figure 6, we use a threshold of 0.5 to consider all
datasets and 1.0 if we need to refine all utterances.

Figure 6: Manual search for the threshold of filtering module. DAE score in
the y-axis indicates the source-faithfulness score after refining the
utterances under the threshold.

##  Appendix E Prompts for Adversarial Data Generation with ChatGPT

The prompt used for generating adversarial data with ChatGPT is in Table 8:

Prompt
---
“Considering the knowledge, persona, and dialogue history below and make the
answer incorrect in terms of factuality. Knowledge: {knowledge}, dialogue
history: {dialogue_history}, answer: {answer}.”

Table 8: The prompt used for generating adversarial data.

##  Appendix F Refining Utterances of Larger Models

|  | Source-faithfulness | Ref. Matching
---|---|---|---
Data | Model | EC | TC | DAE | chrF | R-L
| LLMKGC | 42.74 | 26.72 | 0.84 | 38.16 | 40.64
2-7[1.2pt/1.6pt] FoCus | \+ BART${}_{REM_{large}}$ | 46.06 | 28.76 | 0.91 | 40.95 | 42.36
| LLMKGC | 6.91 |  5.87 | 0.36 | 18.68 | 18.65
2-7[1.2pt/1.6pt] WoW | \+ BART${}_{REM_{large}}$ | 15.97 |  11.15 | 0.77 | 31.08 | 27.44
| LLMKGC |  4.77 |  7.24 | 0.29 | 14.36 | 13.44
2-7[1.2pt/1.6pt] CMUDoG | \+ BART${}_{REM_{large}}$ |  6.27 | 9.63 | 0.43 | 16.10 | 12.38

Table 9: The result of BART${}_{REM_{large}}$ (denoted with +) refining the
KGC utterances of large language model (LLMKGC). The utterances are filtered
with $\tau$ = 0.5

##  Appendix G Human Evaluation Questionnaire

Questionnaire
---
The given sentences from model A and model B are the generated result of each
machine consulting its knowledge to produce informative utterances.
Considering the sentences produced by model A and model B, name the model that
did better on the following three criteria.
Fluent : The response is linguistically fluent and not awkward).
Factually correct : The response is consistent with the given knowledge and
does not generate the facts that are not given.
Well-paraphrased : The response is well-paraphrased rather than just copying
and pasting the given knowledge.

Table 10: An example of the questionnaire for human evaluation.

##  Appendix H Prompts for LLM and LLMREM

The prompts for refining utterances with LLM and LLMREM are in Table 11.

LLM
---
“You are tasked with refining the response given the knowledge, question, and
response. First, you determine whether the response is a factual response
given the knowledge and question. If the response is a factual response
considering the given knowledge and question, output it as it is, and if not,
regenerate it after factually refining the response considering the given
knowledge and question. You should only write "Output" in the format given
without saying why. Knowledge: {knowledge} Response: {response}.”
LLMREM
“You are tasked with refining the response given the knowledge, question, and
response. You determine whether the response is a factual response considering
the given knowledge and question, and if not, extract the necessary entities
to refine the response more factually from the knowledge. Then, refine and
regenerate the response into a factual response considering the given
knowledge and question using the extracted entities. You must write both
"Entities" and "Output" and only "Entities" and "Output" according to the
given format. Knowledge: {knowledge} Response: {response}”

Table 11: The prompts for refining utterance by LLM and LLMREM.

##  Appendix I REM Generation Examples

FoCus
---
|  [Knowledge]
---
The Václav Havel Library, located in Prague, is a charitable organization
founded by Dagmar Havlová, Karel Schwarzenberg and Miloslav Petrusek on 26
July 2004.
[Persona Knowledge]
I love going to libraries. I love reading. I want to visit Prague. I want to
visit New York. I love books.
|  [Utterance]
---
BARTbase : cl Ha Library founded 26 2004.
\+ REMbase : Václav Havel Library was founded on 26 July 2004 by Dagmar
Havlová, Karel Schwarzenberg and Miloslav Petrusek.
|  [Knowledge]
---
The design of the house is attributed to John Verge and John Bibb and was
built from 1835 to 1839 by James Hume.
[Persona Knowledge]
I love colonial architecture. I like Australia. I would like to visit a
museum. I would love to see a grotto. I have read about the Cadigal people.
|  [Utterance]
---
BARTbase : was from35 18 to39
+ REMbase : it was built from 1835 to 1839 by James Hume. I got this information on the internet. It was designed by John Verge and John Bibb.
WoW
|  [Knowledge]
---
A tattoo is a form of body modification where a design is made by inserting
ink, dyes and pigments, either indelible or temporary, into the dermis layer
of the skin to change the pigment.
|  [Utterance]
---
BARTbase : I’m not sure if it is really a tattoo artist, but I do know that
\+ REMbase : of course, it is a form of body modification where a design is
made by inserting ink, dyes and pigments into the dermis layer of the skin to
change the pigment.
|  [Knowledge]
---
It was also applied to the pit-style cooking techniques now frequently used in
the Southeastern United States.
|  [Utterance]
---
BARTbase : , I’ll have to try it sometime! I’ve heard it’s used in the
\+ REMbase : , I don’t know if I’d ever try it. I know that pit-style cooking
techniques are now frequently used in the Southeastern United States.
CMUDoG
|  [Knowledge]
---
Leonardo DiCaprio as Dom Cobb, a professional thief who specializes in conning
secrets from his victims by infiltrating their dreams. Joseph Gordon-Levitt as
Arthur, Cobb’s partner who manages and researches the missions. … Tom Hardy as
Eames, a sharp-tongued associate of Cobb. wildly ingenious chess game, …
|  [Utterance]
---
BARTbase : , have you seen the movie inception?
\+ REMbase : , I’m good, how are you? Have you seen Inception? It’s a science
fiction movie starring Leonardo DiCaprio, Joseph Gordon-Levitt, and Tom Hardy.
|  [Knowledge]
---
Angelina Jolie as Maleficent Sharlto Copley as Stefan Elle Fanning as Aurora
Sam Riley as Diaval Imelda Staunton as Knotgrass Maleficent’ demolishes
stereotypes … Robert Stromberg SuperHero Maleficent is a 2014 American dark
fantasy film directed by Robert Stromberg from a screenplay by Linda
Woolverton, and starring Angelina Jolie as Maleficent with Sharlto Copley,
Elle Fanning, Sam Riley, Imelda Staunton, Juno Temple, and Lesley Manville in
supporting roles. …
|  [Utterance]
---
BARTbase : have you seen Maleficent before?
\+ REMbase : movie we’re talking about is Maleficent. Have you seen it? It’s a
fantasy from 2014. It stars Angelina Jolie, Sharito Copley, Elle Fanning, Sam
Riley, and Imelda Staunton.

Table 12: Examples of the fine-tuned baseline utterances refining by REMbase
on KGC datasets.

FoCus
---
|  [Knowledge]
---
Elizabeth Bay House is a heritage-listed Colonial Regency style house and now
a museum and grotto, located at 7 Onslow Avenue in the inner eastern Sydney
suburb of Elizabeth Bay in the City of Sydney local government area of New
South Wales, Australia.
[Persona Knowledge]
I love colonial architecture. I like Australia. I would like to visit a
museum. I would love to see a grotto. I have read about the Cadigal people.
|  [Utterance]
---
BARTbase : Elizabeth House located Australia which like. is place you like
\+ REMlarge : This place is called Elizabeth Bay House and it is located in
Australia, which you like. It is a heritage-listed Colonial Regency style
house and now a museum and grotto.
|  [Knowledge]
---
The Memphis Zoo, located in Midtown, Memphis, Tennessee, United States, is
home to more than 3,500 animals representing over 500 different species.
[Persona Knowledge]
I hope to visit Tennessee. I like to ride on a tram. I have never seen a
wetland. I would like to see a Giant Panda. I wish I could see a penguin.
|  [Utterance]
---
BARTbase : is Memphis in. is place hope visit.
+ REMlarge : It’s the Memphis Zoo in Tennessee, a place you hope to visit. It is home to more than 3,500 animals, 500 different species.
WoW
|  [Knowledge]
---
Competing theories state that Hindu identity developed in the British colonial
era, or that it developed post-8th century CE after the Islamic invasion and
medieval Hindu-Muslim wars.
|  [Utterance]
---
BARTbase : , I’m not fluent in English, but I understand that Hindu identity
developed in the
\+ REMlarge : No, I’m not fluent in English, but I understand that Hindu
identity developed in the British colonial era, or that it developed post-8th
century CE.
|  [Knowledge]
---
Human occupation of Qatar dates back to 50,000 years ago, and Stone Age
encampments and tools have been unearthed in the peninsula.
|  [Utterance]
---
BARTbase : ’s a pretty busy place, I’ve been to Qatar a few times. It’s
\+ REMlarge : That’s true. It’s a pretty busy place, but it’s been around for
a long time. Human occupation of Qatar dates back 50,000 years ago, and Stone
Age encampments and tools have been found in the peninsula.
CMUDoG
|  [Knowledge]
---
Sally Hawkins as Elisa Esposito, a mute cleaner who works at a secret
government laboratory. … The Shape of Water Rotten Tomatoes: 92% and average:
8.4/10 Metacritic Score: 87/100 CinemaScore: A 2017
|  [Utterance]
---
BARTbase : have you seen The Shape of Water?
\+ REMlarge : Hello, Have you seen The Shape of Water? It’s a really good
movie. It has a 92% on Rotten Tomatoes and a Metacritic Score of 87/100.
|  [Knowledge]
---
Lindsay Lohan as Cady Heron Rachel McAdams as Regina George Lacey Chabert as
Gretchen Wieners Amanda Seyfried as Karen Smith Lizzy Caplan as Janis Ian
Boasts a one-two-three punch in star Lindsay Lohan, screenwriter Tina Fey and
director Mark Waters, and, indeed, it delivers a knockout The screenplay was
highly praised by critics with Peter Travers of Rolling Stone calling it
’comic gold’. …
|  [Utterance]
---
BARTbase : , what did you think of it?
\+ REMlarge : I really liked it, I thought Lindsay Lohan did a great job as
Cady Heron. Tina Fey did an amazing job writing the screenplay as well.

Table 13: Examples of the fine-tuned baseline utterances refining by REMlarge
on KGC datasets.

FoCus
---
|  [Knowledge]
---
hesselberg ( pronounced [ ] ( about this soundlisten ) ; 689 m above sea level
) is the highest point in middle franconia and the franconian jura and is
situated 60 km south west of nuremberg, germany.
[Persona Knowledge]
I love to enjoy nature. I live near Nuremberg. I am from Germany. I like
heights. I study geology.
|  [Utterance]
---
INFO : The name of this place is Hesselberg.
\+ REMlarge : The name of this place is Hesselberg, and it is located 60 km
south west of nuremberg, germany, the country you are from.
|  [Knowledge]
---
st. george’s united methodist church, located at the corner of 4th and new
streets, in the old city neighborhood of philadelphia, is the oldest methodist
church in continuous use in the united states, beginning in 1769.
[Persona Knowledge]
I will stay in Philadelphia. I am going to Pennsylevania. I like history. I am
religious. I am interested in the American Revolution.
|  [Utterance]
---
INFO : This is St. George’s United Methodist Church you may know of it because
you are religious.
+ REMlarge : This is St. George’s United Methodist Church, you may know of it because you are religious. It is the oldest methodist church in continuous use in the united states.
WoW (KILT)
|  [Knowledge]
---
Red Ketchup is a cult Quebec comic book series featuring FBI’s crazed rogue
agent, Steve R̈edK̈etchup. The series was created by Pierre Fournier and Réal
Godbout, and will soon be adapted into a live action feature film by Martin
Villeneuve for GO Films in Montreal.
|  [Utterance]
---
EDMem : ketchup red is a sweet sauce made from tomatoes.
\+ REMlarge : I love red ketchup, which is a cult Quebec comic book series
featuring FBI’s crazed rogue agent, Steve R̈edK̈etchup. The series was created
by Pierre Fournier and Réal Godbout
|  [Knowledge]
---
Childhood obesity is a condition where excess body fat negatively affects a
child’s health or well-being. As methods to determine body fat directly are
difficult, the diagnosis of obesity is often based on BMI. Due to the rising
prevalence of obesity in children and its many adverse health effects it is
being recognized as a serious public health concern. The term overweight
rather than obese is often used when discussing childhood obesity, especially
in open discussion, as it is less stigmatizing.
|  [Utterance]
---
EDMem : i’m not sure, but i do know that in the united states, overweight
people are more likely to be obese than healthy people.
\+ REMlarge : Yea i’m not sure, but the prevalence of obesity in children and
its many adverse health effects it is being recognized as a serious public
health concern.
CMUDoG
|  [Knowledge]
---
Leonardo DiCaprio as Frank Abagnale, Jr. Tom Hanks as Carl Hanratty
Christopher Walken as Frank Abagnale, Sr. Nathalie Baye as Paula Abagnale. …
Catch me if you can Rotten Tomatoes: 96% and average: 7.9/10 Metacritic Score:
76/100 CinemaScore: A- 2002
|  [Utterance]
---
ITDD : yes, it has a 96 % rating on rotten tomatoes.
\+ REMlarge : yes, it has a 96 percent rating on rotten tomatoes and 7.9/10 on
metacritic and a cinema score of an A- which is pretty good.
|  [Knowledge]
---
Mike and Sulley are led away by the CDA, and are expelled from the university
as a result of their actions, but the other members of Oozma Kappa are
accepted into the scare program the next semester, as Hardscrabble was
impressed by their performances in the Games. As Mike leaves on the bus,
Sulley runs after him to raise his spirits. Hardscrabble then appears and
wishes the two luck, claiming they were the first students to have surprised
her. The two take jobs in the mail room of Monsters, Inc., eventually working
their way up to join the scarer team.
|  [Utterance]
---
ITDD : they do
\+ REMlarge : Yes, they do. Sulley is a good friend of Mike’s and tries to
cheer him on when he leaves on the bus to try to raise his spirits.

Table 14: Examples of the existing model utterances refining by REMlarge on
KGC datasets.