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.- Anthology ID:
- 2023.emnlp-main.993
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16009–16020
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.993
- DOI:
- 10.18653/v1/2023.emnlp-main.993
- Cite (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.
- Cite (Informal):
- Automated Fact-Checking in Dialogue: Are Specialized Models Needed? (Chamoun et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.emnlp-main.993.pdf