Automated Fact-Checking of Claims in Argumentative Parliamentary Debates

Nona Naderi, Graeme Hirst


Abstract
We present an automated approach to distinguish true, false, stretch, and dodge statements in questions and answers in the Canadian Parliament. We leverage the truthfulness annotations of a U.S. fact-checking corpus by training a neural net model and incorporating the prediction probabilities into our models. We find that in concert with other linguistic features, these probabilities can improve the multi-class classification results. We further show that dodge statements can be detected with an F1 measure as high as 82.57% in binary classification settings.
Anthology ID:
W18-5509
Volume:
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2018
Address:
Brussels, Belgium
Editors:
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–65
Language:
URL:
https://aclanthology.org/W18-5509
DOI:
10.18653/v1/W18-5509
Bibkey:
Cite (ACL):
Nona Naderi and Graeme Hirst. 2018. Automated Fact-Checking of Claims in Argumentative Parliamentary Debates. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 60–65, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Automated Fact-Checking of Claims in Argumentative Parliamentary Debates (Naderi & Hirst, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5509.pdf