Abstract
Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.- Anthology ID:
- 2020.emnlp-main.623
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7740–7754
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.623
- DOI:
- 10.18653/v1/2020.emnlp-main.623
- Cite (ACL):
- Neema Kotonya and Francesca Toni. 2020. Explainable Automated Fact-Checking for Public Health Claims. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7740–7754, Online. Association for Computational Linguistics.
- Cite (Informal):
- Explainable Automated Fact-Checking for Public Health Claims (Kotonya & Toni, EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.emnlp-main.623.pdf
- Code
- neemakot/Health-Fact-Checking + additional community code
- Data
- PUBHEALTH, FEVER, LIAR, MultiFC, MultiNLI, SNLI