Abstract
A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked. However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline. In this survey, we focus on the explanation functionality – that is fact-checking systems providing reasons for their predictions. We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic. Further, we consider what makes for good explanations in this specific domain through a comparative analysis of existing fact-checking explanations against some desirable properties. Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area.- Anthology ID:
- 2020.coling-main.474
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5430–5443
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.474
- DOI:
- 10.18653/v1/2020.coling-main.474
- Cite (ACL):
- Neema Kotonya and Francesca Toni. 2020. Explainable Automated Fact-Checking: A Survey. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5430–5443, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Explainable Automated Fact-Checking: A Survey (Kotonya & Toni, COLING 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.474.pdf
- Code
- neemakot/Fact-Checking-Survey
- Data
- FEVER, LIAR