How Robust are Fact Checking Systems on Colloquial Claims? (2021)

Abstract: Knowledge is now starting to power neural dialogue agents. At the same time, the risk of misinformation and disinformation from dialogue agents also rises. Verifying the veracity of information from formal sources are widely studied in computational fact checking. In this work, we ask: How robust are fact checking systems on claims in colloquial style? We aim to open up new discussions in the intersection of fact verification and dialogue safety. In order to investigate how fact checking systems behave on colloquial claims, we transfer the styles of claims from FEVER (Thorne et al., 2018) into colloquialism. We find that existing fact checking systems that perform well on claims in formal style significantly degenerate on colloquial claims with the same semantics. Especially, we show that document retrieval is the weakest spot in the system even vulnerable to filler words, such as “yeah” and “you know”. The document recall of WikiAPI retriever (Hanselowski et al., 2018) which is 90.0% on FEVER, drops to 72.2% on the colloquial claims. We compare the characteristics of colloquial claims to those of claims in formal style, and demonstrate the challenging issues in them.

## introduction recently, knowledge has been starting to power neural dialogue agents @xcite @xcite @xcite , being equipped with wikipedia @xcite , news @xcite , domain specific knowledge-base @xcite , and commonsense @xcite @xcite . however, the use of knowledge inevitably put dialogue agents in new jeopardy. for example, recent workshop on safety for conversational ai @xcite ) introduced * equal contribution an example of such risk: @xcite asked participants to query conversational agents for advice in situations where medical information is needed. then, internist and pharmacist judged the actions that the participants would take based on the advice. assessments revealed that agents often deliver incorrect medical information that may cause lethal consequences. a bigger threat may be the abuse of dialogue agents to deliberately distribute disinformation. what would happen if knowledge-powered agents are tweaked to massively generate false claims on online communities? the impact of such fake news can be critical as they quickly spread through social media @xcite . the chatbot tay's shut down due to malicious attempts show the imminent danger of abuse @xcite . verifying the integrity of a given piece of information has been studied in the field of computational fact checking. @xcite introduce an annotated dataset fever for fact checking based on wikipedia. @xcite collect claims on fact checking websites and release the multifc dataset. @xcite collect a dataset requiring many-hop evidence extraction from wikipedia. @xcite collect a dataset of scientific claims to be verified. most claims of existing datasets are taken from formal texts, such as news, academic papers, and wikipedia. these claims tend to be concise and structured: "beautiful was number two on the billboard hot 100 in 2003". on the other hand, claims or information that we encounter in dialogues are more unstructured and informal: "the song beautiful is great! it even reached number two on the hot 100 in 2003, you know?". for improving the applicability of fact checking systems, they must also be robust for verifying the claims in dialogues. unfortunately, threats regarding misinformation and disinformation from dialogue agents remain understudied. research on dialogue safety mainly has focused on making dialogue agents robust to adversarial attacks @xcite , and preventing dialogue agents from generating offensive or biased responses @xcite @xcite . in this work, we aim to investigate how fact checking systems behave when verifying claims in dialogue style, rather than claims from news outlets, scientific articles, or wikipedia. colloquial claims are different in several aspects compared to claims from formal sources. (i) they tend to also include filler words, casual comments, or personal feelings which do not require verification. (ii) since claims in colloquial language are less precise than formal claims, correctly using the context in claims becomes important to disambiguate them. we demonstrate that these features make existing fact checking systems have difficulties in verifying colloquial claims. we use english datasets for the investigation in this work. our major contributions of this work can be outlined as follows: fever @xcite claim: the iphone 4 is a dial telephone. ## transferring to colloquial claims our goal is to curate colloquial claims by transferring the style of each claim sentence in the fever dataset 1 into colloquial style. we first finetune a dialog model with the wow dataset so that it learns to transfer knowledge sentences from wikipedia into conversational utterances (section 3.1). we then apply the finetuned model to transfer each claim in fever (sourced from wikipedia) into colloquial style, and perform filtering process to warrant the integrity of this style transfer (section 3.2). figure 1 overviews the whole pipeline of style transfer. ## experiments and analysis we conduct experiments on our curated colloquial claims to see how they impact existing fact checking systems. ## related work fact checking and verification. the need for claim verification has led to annotated fact check-ing datasets @xcite @xcite @xcite . recent works deploy adversarial attacks against fact checking systems @xcite @xcite and attempt to improve the system through generation @xcite @xcite . existing works tend to focus on verifying news or wikipedia. however, verifying facts is not limited to such formal texts. compared to previous works, we focus on verifying claims in the dialogue domain, which resembles more daily life situations. a special case of fact verification is rumour detection. its goal is to determine the veracity of rumours from social media @xcite . the rumour is classified based on the reactions of chained messages @xcite . the procedure and characteristics of rumour detection is quite different from the fact checking pipeline @xcite . in our task, we verify the claims based on factuality from the related documents, rather than stances of the comments. ## conclusion this work aimed to open up new discussions in the intersection of fact checking and dialogue safety. in order to study how existing fact checking systems behave on claims in dialogues, we curate colloquial claims by transferring the styles of claims in fever @xcite to colloquialism. we leverage bart @xcite and wizard of wikipedia (wow) @xcite . we finetune bart to generate the wizard's responses with knowledge sentences from wow. then, we input fever claims to generate claim-grounded utterances. we oversample candidate claims and apply filters to compensate quality. we showed that existing fact checking systems well-performing on fever degenerate on colloquial claims. we found that the document retriever is the weakest spot in the system which is even vulnerable to filler words. we compared the characteristic differences between claims in formal style and ones in colloquialism . an important future direction will be building a dialogue dataset for fact checking.