Abstract
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 step-by-step. We trained a BioBERT-large classifier to select abstracts based on pairwise relevance assessments for each <claim, title of the abstract> and continued to train it to select rationales out of each retrieved abstract based on <claim, sentence>. 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.- Anthology ID:
- 2021.sdp-1.15
- Volume:
- Proceedings of the Second Workshop on Scholarly Document Processing
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Iz Beltagy, Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Keith Hall, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer, Anita de Waard, Kuansan Wang, Lucy Lu Wang
- Venue:
- sdp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 116–123
- Language:
- URL:
- https://aclanthology.org/2021.sdp-1.15
- DOI:
- 10.18653/v1/2021.sdp-1.15
- Cite (ACL):
- Xia Zeng and Arkaitz Zubiaga. 2021. QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 116–123, Online. Association for Computational Linguistics.
- Cite (Informal):
- QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification (Zeng & Zubiaga, sdp 2021)
- PDF:
- https://preview.aclanthology.org/cschoel_rss_and_blog/2021.sdp-1.15.pdf
- Code
- XiaZeng0223/sciverbinary
- Data
- FEVER, SciFact