Scientific Claim Verification with VerT5erini

Ronak Pradeep, Xueguang Ma, Rodrigo Nogueira, Jimmy Lin


Abstract
This work describes the adaptation of a pretrained sequence-to-sequence model to the task of scientific claim verification in the biomedical domain. We propose a system called VerT5erini that exploits T5 for abstract retrieval, sentence selection, and label prediction, which are three critical sub-tasks of claim verification. We evaluate our pipeline on SciFACT, a newly curated dataset that requires models to not just predict the veracity of claims but also provide relevant sentences from a corpus of scientific literature that support the prediction. Empirically, our system outperforms a strong baseline in each of the three sub-tasks. We further show VerT5erini’s ability to generalize to two new datasets of COVID-19 claims using evidence from the CORD-19 corpus.
Anthology ID:
2021.louhi-1.11
Volume:
Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
Month:
April
Year:
2021
Address:
online
Venues:
EACL | Louhi
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
94–103
Language:
URL:
https://aclanthology.org/2021.louhi-1.11
DOI:
Bibkey:
Cite (ACL):
Ronak Pradeep, Xueguang Ma, Rodrigo Nogueira, and Jimmy Lin. 2021. Scientific Claim Verification with VerT5erini. In Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis, pages 94–103, online. Association for Computational Linguistics.
Cite (Informal):
Scientific Claim Verification with VerT5erini (Pradeep et al., Louhi 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.louhi-1.11.pdf
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