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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W18-5509.pdf