It takes nine to smell a rat: Neural multi-task learning for check-worthiness prediction

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We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different fact-checking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.

Original languageEnglish
Title of host publicationInternational Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings
EditorsGalia Angelova, Ruslan Mitkov, Ivelina Nikolova, Irina Temnikova, Irina Temnikova
PublisherIncoma Ltd
Publication date2019
Pages1229-1239
ISBN (Electronic)9789544520557
DOIs
Publication statusPublished - 2019
Event12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019 - Varna, Bulgaria
Duration: 2 Sep 20194 Sep 2019

Conference

Conference12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019
LandBulgaria
ByVarna
Periode02/09/201904/09/2019
SeriesInternational Conference Recent Advances in Natural Language Processing, RANLP
Volume2019-September
ISSN1313-8502

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