QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification
Xia Zeng, Arkaitz Zubiaga · 2021
Scientific claim verification is a unique challenge that is attracting increasing interest.The SCIVER shared task offers a benchmark scenario to test and compare claim verification approaches by participating teams and consists in three steps: relevant abstract selection, rationale selection and label prediction.In this paper, we present team QMUL-SDS's participation in the shared task.We propose an approach that performs scientific claim verification by doing binary classifications stepby-step.We trained a BioBERT-large classifier to select abstracts based on pairwise relevance assessments for each and continued to train it to select rationales out of each retrieved abstract based on .We then propose a two-step setting for label prediction, i.e. first predicting "NOT_ENOUGH_INFO" or "ENOUGH_INFO", then label those marked as "ENOUGH_INFO" as either "SUPPORT" or "CONTRADICT".Compared to the baseline system, we achieve substantial improvements on the dev set.As a result, our team is the No. 4 team on the leaderboard.