We present an overview of the SCIVER shared task, presented at the 2nd Scholarly Document Processing (SDP) workshop at NAACL 2021. In this shared task, systems were provided a scientific claim and a corpus of research abstracts, and asked to identify
which articles Support or Refute the claim as well as provide evidentiary sentences justifying those labels. 11 teams made a total of 14 submissions to the shared task leaderboard, leading to an improvement of more than +23 F1 on the primary task evaluation metric. In addition to surveying the participating systems, we provide several insights into modeling approaches to support continued progress and future research on the important and challenging task of scientific claim verification.
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, an
d 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.
