Automated Fact-Checking in Dialogue: Are Specialized Models Needed? (2023)

Abstract: Prior research has shown that typical fact-checking models for stand-alone claims struggle with claims made in conversation. As a solution, fine-tuning these models on dialogue data has been proposed. However, creating separate models for each use case is impractical, and we show that fine-tuning models for dialogue results in poor performance on typical fact-checking. To overcome this challenge, we present techniques that allow us to use the same models for both dialogue and typical fact-checking. These mainly focus on retrieval adaptation and transforming conversational inputs so that they can be accurately processed by models trained on stand-alone claims. We demonstrate that a typical fact-checking model incorporating these techniques is competitive with state-of-the-art models for dialogue, while maintaining its performance on stand-alone claims.

## introduction the need for fact-checking is ever-growing as the volume of false claims on social media platforms rises, inspiring researchers to develop automated tools to combat misinformation @xcite . despite the application of automated fact-checking to various use cases, most studies still focus on stand-alone, well-formed claims similar to those found in formal sources like encyclopedias. however, such claims are different from those found in conversations, which often feature incomplete utterances that reference previously mentioned entities or even omit them @xcite . additionally, conversational utterances often include filler words and casual comments, which complicate the task. recently, @xcite presented dialfact, a dataset for automated fact-checking in dialogue. their experiments showed that state-of-the-art models, trained on stand-alone well-formed claims, do not perform well on dialfact. to address this, they propose instead to fine-tune models on conversational claims within their dialogue contexts. in this paper, we first demonstrate that although these models obtain strong results on dialfact, they suffer from a significant decrease in accuracy on stand-alone fact-checking, a form of catastrophic forgetting, i.e. the tendency of a model to forget previously learned abilities after learning from new data @xcite . furthermore, we argue that building a separate model for every real-world scenario is not a practical solution, since each model requires ongoing monitoring and maintenance for long-term reliability @xcite . for these reasons we introduce methods for adapted evidence retrieval and input transformation without changing the fact-checking model. we first present a claim detection technique to tackle the low density of factual information in dialogue claims (figure 1 , in red). additionally, we modify document retrieval to handle both the conversational context and the claim, but place more weight on the latter to reduce noisy retrieval results (figure 1 , in blue). finally, we enhance sentence retrieval by considering not only the relevance of the retrieved sentence to the claim, but also that of the document it is sourced from (figure 1 , in green). by incorporating the proposed techniques, a typical fact-checking model can match the performance of state-of-the-art models fine-tuned specifically for dialogue on dialfact, while maintaining its accuracy on fever @xcite , a benchmark for stand-alone well-formed claims. in comparison, fine-tuning the same model for dialogue results in a minimum reduction of 12% accuracy on fever due to catastrophic forgetting. ## fact-checking conversational claims fact-checking systems typically consist of three components: a document retriever that returns the most relevant documents to a given claim from a textual source such as wikipedia, a sentence retriever that selects the most relevant evidence sentence(s) from the retrieved documents, and a claim verification model that classifies the claim as sup-ports, refutes or not enough info (nei) w.r.t. the evidence. we formulate fact-checking dialogue claims as the task of verifying the last utterance, referred to as the claim c, of a multiturn conversation @xmath0, ..., u n-1 , c}. this section presents techniques aimed at improving the dialogue fact-checking performance of a model trained on stand-alone well-formed claims, without requiring any adaptation of the model itself. ## results implementation details fever @xcite is a benchmark dataset comprising wellformed claims derived from wikipedia. our approach builds on the state of the art. it employs genre foot_0 @xcite as our scoring function f for document retrieval following @xcite . for sentence retrieval and claim verification, we leverage the bigbird-based @xcite and deberta @xcite ) models foot_1 respectively, from @xcite . for the evidence enhancement ensemble, we train a logistic regression model g using the scikit-learn library foot_2 . for claim detection, we leverage sroberta foot_3 and spacy's sentencizer foot_4 to perform sentence encoding and sentence splitting, respectively. finally, we used spanbert @xcite for coreference resolution, styleformer foot_5 for style transfer, and the gpt-2-based model proposed by @xcite for claim rewrite. ## qualitative analysis we conducted a qualitative analysis to assess the advantages and limitations of different approaches. we initially analyzed the performance improvements offered by our proposed techniques when incorporated to typical fact-checking models by focusing on cases where the central entities in the claim were referred to using pronouns. our findings showed that most of the times, the document retrieval technique we proposed was still able to successfully identify the appropriate document by taking into account the context (example 1 in table b.1). in our study of claims with coreference, we encountered multiple situations where document retrieval was successful but returned multiple documents with similar potential evidence sentences (e.g., birth years of "pope paul i" and "paul mccartney", figure 1 ). our proposed sentence retrieval enhancement technique played a crucial role in these cases by using the context gathered during document retrieval to select the right evidence, resulting in more accurate predictions (example 2 in table b .1). we also evaluated instances where the claim contained limited factual information. frequently, our claim detection method effectively filtered out irrelevant sentences and allowed only the essential information to be processed by the model, leading to accurate predictions (example 3 in table b .1). additionally, we studied instances where specialized models for dialogue fact-checking outperformed the typical fact-checking model combining our techniques. we found that a common challenge in conversational claims that our approach does not address is indirect claims, such as those made in the form of a question. this limitation is due to the fact that typical fact-checking models are not trained to recognize indirect claims, and our proposed claim detection technique does not resolve this issue (example 4 in table b .1). finally, we examined cases where fever+vitc+retrieval+claimdet outperforms specialized models for dialogue fact-checking. we discovered that in instances where a conversational claim could be easily transformed into a well-formed claim through methods such as claim detection, typical fact-checking models often outperformed those specifically designed for dialogue fact-checking. this is because the latter models experience catastrophic forgetting (example 5 in table b .1). ## conclusion this paper highlights the significant catastrophic forgetting effects that result from adapting a typical fact-checking model for dialogue. we argue that using separate models for each task is not practical due to the ongoing maintenance cost attached to each. instead, we propose techniques that allow us to use the same model for both use cases. these mainly focus on retrieval and input adaptation. the model combining these techniques performs comparably to the top specialized models on dialfact while substantially outperforming them on fever. we discuss the limitations and societal impact of our approach in the model card @xcite .