@inproceedings{pathak-etal-2020-self,
title = "Self-Supervised Claim Identification for Automated Fact Checking",
author = "Pathak, Archita and
Shaikh, Mohammad Abuzar and
Srihari, Rohini",
editor = "Bhattacharyya, Pushpak and
Sharma, Dipti Misra and
Sangal, Rajeev",
booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2020",
address = "Indian Institute of Technology Patna, Patna, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2020.icon-main.28",
pages = "213--227",
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 \textit{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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Self-Supervised Claim Identification for Automated Fact Checking
%A Pathak, Archita
%A Shaikh, Mohammad Abuzar
%A Srihari, Rohini
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F pathak-etal-2020-self
%X 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.
%U https://aclanthology.org/2020.icon-main.28
%P 213-227
Markdown (Informal)
[Self-Supervised Claim Identification for Automated Fact Checking](https://aclanthology.org/2020.icon-main.28) (Pathak et al., ICON 2020)
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).