Generating Fact Checking Explanations
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Generating Fact Checking Explanations. / Atanasova, Pepa; Simonsen, Jakob Grue; Lioma, Christina; Augenstein, Isabelle.
: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2020. p. 7352-7364.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Generating Fact Checking Explanations
AU - Atanasova, Pepa
AU - Simonsen, Jakob Grue
AU - Lioma, Christina
AU - Augenstein, Isabelle
PY - 2020
Y1 - 2020
N2 - Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process – generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model.
AB - Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process – generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model.
U2 - 10.18653/v1/2020.acl-main.656
DO - 10.18653/v1/2020.acl-main.656
M3 - Article in proceedings
SP - 7352
EP - 7364
BT - : Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
PB - Association for Computational Linguistics
T2 - 58th Annual Meeting of the Association for Computational Linguistics
Y2 - 5 July 2020 through 10 July 2020
ER -
ID: 254776315