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

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Dokumenter

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.

OriginalsprogEngelsk
TitelInternational Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings
RedaktørerGalia Angelova, Ruslan Mitkov, Ivelina Nikolova, Irina Temnikova, Irina Temnikova
ForlagIncoma Ltd
Publikationsdato2019
Sider1229-1239
ISBN (Elektronisk)9789544520557
DOI
StatusUdgivet - 2019
Begivenhed12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019 - Varna, Bulgarien
Varighed: 2 sep. 20194 sep. 2019

Konference

Konference12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019
LandBulgarien
ByVarna
Periode02/09/201904/09/2019
NavnInternational Conference Recent Advances in Natural Language Processing, RANLP
Vol/bind2019-September
ISSN1313-8502

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