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        <title>Multiple Evidence Combination for Fact-Checking of Health-Related Information</title>
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    <abstract>Fact-checking of health-related claims has become necessary in this digital age, where any information posted online is easily available to everyone. The most effective way to verify such claims is by using evidences obtained from reliable sources of medical knowledge, such as PubMed. Recent advances in the field of NLP have helped automate such fact-checking tasks. In this work, we propose a domain-specific BERT-based model using a transfer learning approach for the task of predicting the veracity of claim-evidence pairs for the verification of health-related facts. We also improvise on a method to combine multiple evidences retrieved for a single claim, taking into consideration conflicting evidences as well. We also show how our model can be exploited when labelled data is available and how back-translation can be used to augment data when there is data scarcity.</abstract>
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    <identifier type="doi">10.18653/v1/2023.bionlp-1.20</identifier>
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%0 Conference Proceedings
%T Multiple Evidence Combination for Fact-Checking of Health-Related Information
%A Deka, Pritam
%A Jurek-Loughrey, Anna
%A P, Deepak
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F deka-etal-2023-multiple
%X Fact-checking of health-related claims has become necessary in this digital age, where any information posted online is easily available to everyone. The most effective way to verify such claims is by using evidences obtained from reliable sources of medical knowledge, such as PubMed. Recent advances in the field of NLP have helped automate such fact-checking tasks. In this work, we propose a domain-specific BERT-based model using a transfer learning approach for the task of predicting the veracity of claim-evidence pairs for the verification of health-related facts. We also improvise on a method to combine multiple evidences retrieved for a single claim, taking into consideration conflicting evidences as well. We also show how our model can be exploited when labelled data is available and how back-translation can be used to augment data when there is data scarcity.
%R 10.18653/v1/2023.bionlp-1.20
%U https://aclanthology.org/2023.bionlp-1.20
%U https://doi.org/10.18653/v1/2023.bionlp-1.20
%P 237-247

Markdown (Informal)

[Multiple Evidence Combination for Fact-Checking of Health-Related Information](https://aclanthology.org/2023.bionlp-1.20) (Deka et al., BioNLP 2023)

• Multiple Evidence Combination for Fact-Checking of Health-Related Information (Deka et al., BioNLP 2023)

ACL

• Pritam Deka, Anna Jurek-Loughrey, and Deepak P. 2023. Multiple Evidence Combination for Fact-Checking of Health-Related Information. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 237–247, Toronto, Canada. Association for Computational Linguistics.