Claim matching beyond english to scale global fact-checking

Ashkan Kazemi, Devin Gaffney, Kiran Garimella, Scott A. Hale

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

Abstract

Manual fact-checking does not scale well to serve the needs of the internet. This issue is further compounded in non-English contexts. In this paper, we discuss claim matching as a possible solution to scale fact-checking. We define claim matching as the task of identifying pairs of textual messages containing claims that can be served with one fact-check. We construct a novel dataset of WhatsApp tipline and public group messages alongside fact-checked claims that are first annotated for containing “claim-like statements” and then matched with potentially similar items and annotated for claim matching. Our dataset contains content in high-resource (English, Hindi) and lower-resource (Bengali, Malayalam, Tamil) languages. We train our own embedding model using knowledge distillation and a high-quality “teacher” model in order to address the imbalance in embedding quality between the low- and high-resource languages in our dataset. We provide evaluations on the performance of our solution and compare with baselines and existing state-of-the-art multilingual embedding models, namely LASER and LaBSE. We demonstrate that our performance exceeds LASER and LaBSE in all settings. We release our annotated datasets, codebooks, and trained embedding model2 to allow for further research.

Original languageEnglish (US)
Title of host publicationACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages4504-4517
Number of pages14
ISBN (Electronic)9781954085527
DOIs
StatePublished - 2021
Externally publishedYes
EventJoint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021 - Virtual, Online
Duration: Aug 1 2021Aug 6 2021

Publication series

NameACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference

Conference

ConferenceJoint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021
CityVirtual, Online
Period8/1/218/6/21

All Science Journal Classification (ASJC) codes

  • Software
  • Computational Theory and Mathematics
  • Linguistics and Language
  • Language and Linguistics

Fingerprint

Dive into the research topics of 'Claim matching beyond english to scale global fact-checking'. Together they form a unique fingerprint.

Cite this