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Erschienen in: KI - Künstliche Intelligenz 3-4/2022

13.09.2022 | Dissertation and Habilitation Abstracts

Habilitation Abstract: Towards Explainable Fact Checking

verfasst von: Isabelle Augenstein

Erschienen in: KI - Künstliche Intelligenz | Ausgabe 3-4/2022

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Abstract

With the substantial rise in the amount of mis- and disinformation online, fact checking has become an important task to automate. This article is a summary of a habilitation (doctor scientiarum) thesis submitted to the University of Copenhagen, which was sucessfully defended in December 2021 (Augenstein in Towards Explainable Fact Checking. Dr. Scient. thesis, University of Copenhagen, Faculty of Science, 2021). The dissertation addresses several fundamental research gaps within automatic fact checking. The contributions are organised along three verticles: (1) the fact-checking subtask they address; (2) methods which only require small amounts of manually labelled data; (3) methods for explainable fact checking, addressing the problem of opaqueness in the decision-making of black-box fact checking models.

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Literatur
5.
Zurück zum Zitat Augenstein I, Lioma C, Wang D, Chaves Lima L, Hansen C, Hansen C, Simonsen JG (2019) MultiFC: a real-world multi-domain dataset for evidence-based fact checking of claims. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong, China. https://www.aclweb.org/anthology/D19-1475. Accessed 1 May 2022 Augenstein I, Lioma C, Wang D, Chaves Lima L, Hansen C, Hansen C, Simonsen JG (2019) MultiFC: a real-world multi-domain dataset for evidence-based fact checking of claims. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong, China. https://​www.​aclweb.​org/​anthology/​D19-1475. Accessed 1 May 2022
6.
Zurück zum Zitat Augenstein I, Rocktäschel T, Vlachos A, Bontcheva K (2016) Stance detection with bidirectional conditional encoding. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP). Austin, Texas. https://www.aclweb.org/anthology/D16-1084. Accessed 1 May 2022 Augenstein I, Rocktäschel T, Vlachos A, Bontcheva K (2016) Stance detection with bidirectional conditional encoding. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP). Austin, Texas. https://​www.​aclweb.​org/​anthology/​D16-1084. Accessed 1 May 2022
7.
Zurück zum Zitat Augenstein I, Ruder S, Søgaard A (2018) Multi-task learning of pairwise sequence classification tasks over disparate label spaces. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Volume 1 (Long Papers). New Orleans, Louisiana. https://www.aclweb.org/anthology/N18-1172. Accessed 1 May 2022 Augenstein I, Ruder S, Søgaard A (2018) Multi-task learning of pairwise sequence classification tasks over disparate label spaces. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Volume 1 (Long Papers). New Orleans, Louisiana. https://​www.​aclweb.​org/​anthology/​N18-1172. Accessed 1 May 2022
11.
Zurück zum Zitat Zubiaga A, Kochkina E, Liakata M, Procter R, Lukasik M, Bontcheva K, Cohn T, Augenstein I (2018) Discourse-aware rumour stance classification in social media using sequential classifiers. Inf Process Manag 54(2):273–290. Accessed 1 May 2022CrossRef Zubiaga A, Kochkina E, Liakata M, Procter R, Lukasik M, Bontcheva K, Cohn T, Augenstein I (2018) Discourse-aware rumour stance classification in social media using sequential classifiers. Inf Process Manag 54(2):273–290. Accessed 1 May 2022CrossRef
Metadaten
Titel
Habilitation Abstract: Towards Explainable Fact Checking
verfasst von
Isabelle Augenstein
Publikationsdatum
13.09.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
KI - Künstliche Intelligenz / Ausgabe 3-4/2022
Print ISSN: 0933-1875
Elektronische ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-022-00774-6

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