It takes nine to smell a rat: Neural multi-task learning for check-worthiness prediction
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- It Takes Nine to Smell a Rat
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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 language | English |
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Title of host publication | International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings |
Editors | Galia Angelova, Ruslan Mitkov, Ivelina Nikolova, Irina Temnikova, Irina Temnikova |
Publisher | Incoma Ltd |
Publication date | 2019 |
Pages | 1229-1239 |
ISBN (Electronic) | 9789544520557 |
DOIs | |
Publication status | Published - 2019 |
Event | 12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019 - Varna, Bulgaria Duration: 2 Sep 2019 → 4 Sep 2019 |
Conference
Conference | 12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019 |
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Land | Bulgaria |
By | Varna |
Periode | 02/09/2019 → 04/09/2019 |
Series | International Conference Recent Advances in Natural Language Processing, RANLP |
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Volume | 2019-September |
ISSN | 1313-8502 |
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