DialFact: A Benchmark for Fact-Checking in Dialogue
Prakhar Gupta, Chien-Sheng Wu, Wenhao Liu, Caiming Xiong · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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 (Thorne et al., 2018) 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 1 .Dialogue Context: I have family in Ireland!Have you ever been there?Evidence: Ireland is an island in the North Atlantic.Non-Verifiable Response: I haven't been but want to!Verifiable Supported Response: I haven't.It is an island in the north Atlantic right?Verifiable Refuted Response: I haven't been.Isn't it somewhere in north Pacific?Verifiable NEI Response: I haven't been.I heard it's the most popular tourist location in Europe!