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        <title>Automated Fact-Checking in Dialogue: Are Specialized Models Needed?</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Automated Fact-Checking in Dialogue: Are Specialized Models Needed?
%A Chamoun, Eric
%A Saeidi, Marzieh
%A Vlachos, Andreas
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chamoun-etal-2023-automated
%X 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.
%R 10.18653/v1/2023.emnlp-main.993
%U https://aclanthology.org/2023.emnlp-main.993
%U https://doi.org/10.18653/v1/2023.emnlp-main.993
%P 16009-16020

Markdown (Informal)

[Automated Fact-Checking in Dialogue: Are Specialized Models Needed?](https://aclanthology.org/2023.emnlp-main.993) (Chamoun et al., EMNLP 2023)

• Automated Fact-Checking in Dialogue: Are Specialized Models Needed? (Chamoun et al., EMNLP 2023)

ACL

• Eric Chamoun, Marzieh Saeidi, and Andreas Vlachos. 2023. Automated Fact-Checking in Dialogue: Are Specialized Models Needed?. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16009–16020, Singapore. Association for Computational Linguistics.