Explainable Automated Fact-Checking for Public Health Claims
File(s)
Accepted version
Author(s)
Kotonya, Neema
Toni, Francesca
Type
Conference Paper
Abstract
Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast major-ity of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new datasetPUBHEALTHof 11.8K claims accompanied by journalist crafted, gold standard explanations(i.e., judgments) to support the fact-check la-bels for claims1. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that,by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.
Editor(s)
Webber, B
Cohn, T
He, Y
Liu, Y
Date Issued
2020-11-01
Date Acceptance
2020-09-15
Citation
https://www.aclweb.org/anthology/volumes/2020.emnlp-main/, 2020, pp.7740-7754
Publisher
ACL
Start Page
7740
End Page
7754
Journal / Book Title
https://www.aclweb.org/anthology/volumes/2020.emnlp-main/
Copyright Statement
©2020 Association for Computational Linguistics
Source
2020 Conference on Empirical Methods in Natural Language Processing (EMNLP(1) 2020)
Publication Status
Published
Start Date
2020-11-16
Finish Date
2020-11-20
Coverage Spatial
Online