Where is Your Evidence: Improving Fact-checking by Justification Modeling

Tariq Alhindi, Savvas Petridis, Smaranda Muresan


Abstract
Fact-checking is a journalistic practice that compares a claim made publicly against trusted sources of facts. Wang (2017) introduced a large dataset of validated claims from the POLITIFACT.com website (LIAR dataset), enabling the development of machine learning approaches for fact-checking. However, approaches based on this dataset have focused primarily on modeling the claim and speaker-related metadata, without considering the evidence used by humans in labeling the claims. We extend the LIAR dataset by automatically extracting the justification from the fact-checking article used by humans to label a given claim. We show that modeling the extracted justification in conjunction with the claim (and metadata) provides a significant improvement regardless of the machine learning model used (feature-based or deep learning) both in a binary classification task (true, false) and in a six-way classification task (pants on fire, false, mostly false, half true, mostly true, true).
Anthology ID:
W18-5513
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:
85–90
Language:
URL:
https://aclanthology.org/W18-5513
DOI:
10.18653/v1/W18-5513
Bibkey:
Cite (ACL):
Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018. Where is Your Evidence: Improving Fact-checking by Justification Modeling. In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 85–90, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Where is Your Evidence: Improving Fact-checking by Justification Modeling (Alhindi et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5513.pdf
Code
 Tariq60/LIAR-PLUS +  additional community code
Data
LIAR