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

Slavena Vasileva, Pepa Atanasova, Lluís Màrquez, Alberto Barrón-Cedeño, Preslav Nakov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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
Pages1229-1239
Number of pages11
ISBN (Electronic)9789544520557
DOIs
Publication statusPublished - 1 Jan 2019
Event12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019 - Varna, Bulgaria
Duration: 2 Sep 20194 Sep 2019

Publication series

NameInternational Conference Recent Advances in Natural Language Processing, RANLP
Volume2019-September
ISSN (Print)1313-8502

Conference

Conference12th International Conference on Recent Advances in Natural Language Processing, RANLP 2019
CountryBulgaria
CityVarna
Period2/9/194/9/19

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ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Vasileva, S., Atanasova, P., Màrquez, L., Barrón-Cedeño, A., & Nakov, P. (2019). It takes nine to smell a rat: Neural multi-task learning for check-worthiness prediction. In G. Angelova, R. Mitkov, I. Nikolova, I. Temnikova, & I. Temnikova (Eds.), International Conference on Recent Advances in Natural Language Processing in a Deep Learning World, RANLP 2019 - Proceedings (pp. 1229-1239). (International Conference Recent Advances in Natural Language Processing, RANLP; Vol. 2019-September). Incoma Ltd. https://doi.org/10.26615/978-954-452-056-4_141