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


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.
Anthology ID:
R19-1141
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1229–1239
Language:
URL:
https://aclanthology.org/R19-1141
DOI:
10.26615/978-954-452-056-4_141
Bibkey:
Cite (ACL):
Slavena Vasileva, Pepa Atanasova, Lluís Màrquez, Alberto Barrón-Cedeño, and Preslav Nakov. 2019. It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1229–1239, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction (Vasileva et al., RANLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/R19-1141.pdf