D ial F act: A Benchmark for Fact-Checking in Dialogue (2022)

Abstract: Fact-checking is an essential tool to mitigate the spread of misinformation and disinformation. We introduce the task of fact-checking in dialogue, which is a relatively unexplored area. We construct DialFact, a testing benchmark dataset of 22,245 annotated conversational claims, paired with pieces of evidence from Wikipedia. There are three sub-tasks in DialFact: 1) Verifiable claim detection task distinguishes whether a response carries verifiable factual information; 2) Evidence retrieval task retrieves the most relevant Wikipedia snippets as evidence; 3) Claim verification task predicts a dialogue response to be supported, refuted, or not enough information. We found that existing fact-checking models trained on non-dialogue data like FEVER fail to perform well on our task, and thus, we propose a simple yet data-efficient solution to effectively improve fact-checking performance in dialogue. We point out unique challenges in DialFact such as handling the colloquialisms, coreferences, and retrieval ambiguities in the error analysis to shed light on future research in this direction.

## introduction misinformation online can have deleterious consequences to our society, especially during public health crises like the covid-19 pandemic. false and outdated information can be spread not only by humans but also by automatic agents as generative models have shown remarkable progress recently @xcite . these systems are not perfect, as they can either generate hallucinated and imperfect information, or they can be abused to automatically generate false claims and spread misinformation at a massive scale. fact verification tools are thus necessary in the current information age to tackle the spread of misinformation propagated. figure 1 : dialogue fact-checking involves predicting if a response should be considered a verifiable claim, followed by finding relevant evidence, and finally predicting if the it is supported, refuted or nei. fact-checking was introduced in wang (2017); @xcite and since then a growing body of research has explored and suggested various tasks and resources to address the challenges in this area. fact-checking has been explored in medium such as wikipedia passages, tables, social media and news articles @xcite . in dialogue domain, related work either focus on evaluating factual consistency @xcite or consistent response generation @xcite . however, due to lack of publicly available benchmarks, fact checking is still underexplored in the dialogue domain. verifying factual correctness of claims in dialogue poses new challenges to both dataset construction and modeling. claims in existing datasets are from formal sources such as news articles and they are generally succinct and formal. in contrast, claims in dialogue are often informal and sparse in factual content. furthermore, dialogue utterances often include personal opinions, slang, and colloquialisms which need to be distinguished from factual information. another challenge in dialogue fact-checking is that ellipsis and coreference occur frequently which make utterances incomplete and ambiguous @xcite . although humans can easily understand utterances with refer-ences or absent information based on the dialogue context and their reasoning skills, a fact-checking system may need to model this behavior explicitly. we introduce the task of fact-checking in dialogue and propose an evaluation dataset, dial-fact. an example is shown in figure 1 . di-alfact has three sub-tasks: 1) verifiable claim detection aims to distinguish responses that do not contain verifiable factual information, such as "i haven't been but want to!" in figure 1 . 2) evidence retrieval involves selecting the most relevant knowledge snippets from wikipedia which can verify the response. 3) claim verification aims to classify if a response is supported, refuted, or does not have enough information to verify the response given the dialogue history and the retrieved evidence. dialfact consists of both human-written and machine-generated claims based on the wizard of wikipedia @xcite dialogue dataset. each response claim and its evidence sentences from wikipedia are annotated by crowd workers and we perform rigorous quality checks on the annotations. for fact verification, we propose creation of weakly-supervised training data by leveraging techniques such as negation, entity swapping, language model mask-and-fill, and knowledgegrounded generation. we establish baseline model performance on this task, and point out the weaknesses of fact-checking models. our analysis show that this is a non-trivial task with challenges remaining for future work. we hope that future work can leverage this dataset as a fact-checking benchmark or for development of automatic consistency metrics, and advance the state-of-the art in knowledgegrounded dialogue generation and evaluation. ## related work fact verification the spread of false information online has led to a growing body of research exploring automatic fact-checking. @xcite and subsequent works @xcite @xcite introduced fact extraction and verification datasets verifiable against pieces of evidence from wikipedia articles. factchecking has been explored in a variety of mediums such as wikipedia based claims @xcite , claims over tables @xcite , scientific claims @xcite , and social media claims @xcite . however, fact-checking in dialogue is still an underexplored area. @xcite explored fact-checking for colloquial claims, curated by converting fever claims into colloquial style. although closely related to our work, colloquial claims is not a dialogue dataset, only contains verifiable claims, and does not have dialogue contexts for claims. in dialfact, on the other hand, both evidence retrieval and claim verification are more challenging as they require resolving ambiguities and coreferences from the dialogue context. consistency in dialogue neural dialogue systems grounded on knowledge sources such as wikipedia @xcite , knowledge graphs @xcite or snippets from the internet @xcite have garnered interest in recent years. despite generating plausible and engaging responses, existing models still hallucinate invalid information @xcite . ensuring safety and consistency in dialogue response generation is thus an actively explored area @xcite . some recent works have proposed evaluation metrics and benchmarks for factual consistency in knowledge grounded response generation @xcite . our work instead focuses on factchecking in dialogue for both human and machinegenerated responses, and involves additional tasks of verifiable claim detection and evidence retrieval. synthetic datasets synthetic dataset construction has been shown to improve robustness of evaluation models @xcite and improve the complexity of test sets @xcite . synthetic claims have been explored in fact-checking to create hard test sets. several participants in the fever 2.0 breakers phase @xcite @xcite proposed approaches for automatically generated adversarial claims. recently, jiang et al. (2020) created complex multihop claims using word substitutions, @xcite used bert based token-infilling to created refuted claims, and @xcite created synthetic revisions to wikipedia sentences to improve fact-checking robustness. our work also introduces techniques to create synthetic claims in the context of dialogue fact-checking. ## task background let a conversation context consist of a list of utterances @xmath0, u 2 , ..., u n }. the task is to perform fact-checking on the last utterance of the conversation u n , henceforth called claim c. factchecking claims in conversations is a pipeline that consists of several steps. first, the system needs to decide whether a response is verifiable or non-verifiable. we define them as follows: ## dataset construction and annotation we use two approaches to create claim responses for dialfact: 1) automatically generated claims, and 2) human written claims to emulates claims created by dialogue systems and humans respectively. all claims are further annotated by crowd workers on amazon mechanical turk (mturk). ## experiments we propose new baselines and compare with existing models for three sub-tasks in dialogue factchecking -1) verifiable claim detection, 2) evidence retrieval, and 3) claim verification. ## conclusion we propose a new benchmark, dialfact, for factchecking in dialogue created based on grounded dialogues from the wizard-of-wikipedia dataset. besides human-written response claims, we also create synthetic claims with operations such as contradiction, infilling and substitutions. we hire qualified crowd workers to annotate responses into non-verifiable, supported, refuted, or note-noughinformation categories along with corresponding evidence. we point out empirically that existing fact-checking models trained on nondialogue data fail to perform well on our task. we demonstrate how to leverage automatically generated responses as weak supervised signals to improve performance. we hope that dialfact can facilitate fact-checking, and consistency modeling and evaluation research in the dialogue community.