Self-Supervised Claim Identification for Automated Fact Checking

Archita Pathak, Mohammad Abuzar Shaikh, Rohini Srihari


Abstract
We propose a novel, attention-based self-supervised approach to identify “claim-worthy” sentences in a fake news article, an important first step in automated fact-checking. We leverage aboutness of headline and content using attention mechanism for this task. The identified claims can be used for downstream task of claim verification for which we are releasing a benchmark dataset of manually selected compelling articles with veracity labels and associated evidence. This work goes beyond stylistic analysis to identifying content that influences reader belief. Experiments with three datasets show the strength of our model.
Anthology ID:
2020.icon-main.28
Volume:
Proceedings of the 17th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2020
Address:
Indian Institute of Technology Patna, Patna, India
Editors:
Pushpak Bhattacharyya, Dipti Misra Sharma, Rajeev Sangal
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
213–227
Language:
URL:
https://aclanthology.org/2020.icon-main.28
DOI:
Bibkey:
Cite (ACL):
Archita Pathak, Mohammad Abuzar Shaikh, and Rohini Srihari. 2020. Self-Supervised Claim Identification for Automated Fact Checking. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 213–227, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Self-Supervised Claim Identification for Automated Fact Checking (Pathak et al., ICON 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/2020.icon-main.28.pdf
Code
 architapathak/Self-Supervised-ClaimIdentification