Scientific Claim Verification with VERT5ERINI
This work addresses the problem of verifying scientific claims with evidence from literature for researchers and practitioners, representing an incremental improvement over existing methods.
The authors tackled scientific claim verification in biomedicine by adapting a pretrained T5 model into the VERT5ERINI pipeline for abstract retrieval, sentence selection, and label prediction, outperforming a strong baseline on the SCIFACT dataset and demonstrating generalization to COVID-19 claims using the CORD-19 corpus.
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 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 this decision. Empirically, our pipeline outperforms a strong baseline in each of the three steps. Finally, we show VERT5ERINI's ability to generalize to two new datasets of COVID-19 claims using evidence from the ever-expanding CORD-19 corpus.