@inproceedings{kotonya-toni-2020-explainable,
title = "Explainable Automated Fact-Checking: A Survey",
author = "Kotonya, Neema and
Toni, Francesca",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.474/",
doi = "10.18653/v1/2020.coling-main.474",
pages = "5430--5443",
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."
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%0 Conference Proceedings
%T Explainable Automated Fact-Checking: A Survey
%A Kotonya, Neema
%A Toni, Francesca
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F kotonya-toni-2020-explainable
%X 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.
%R 10.18653/v1/2020.coling-main.474
%U https://aclanthology.org/2020.coling-main.474/
%U https://doi.org/10.18653/v1/2020.coling-main.474
%P 5430-5443
Markdown (Informal)
[Explainable Automated Fact-Checking: A Survey](https://aclanthology.org/2020.coling-main.474/) (Kotonya & Toni, COLING 2020)
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.