@inproceedings{zeng-zubiaga-2021-qmul,
    title = "{QMUL}-{SDS} at {SCIVER}: Step-by-Step Binary Classification for Scientific Claim Verification",
    author = "Zeng, Xia  and
      Zubiaga, Arkaitz",
    editor = "Beltagy, Iz  and
      Cohan, Arman  and
      Feigenblat, Guy  and
      Freitag, Dayne  and
      Ghosal, Tirthankar  and
      Hall, Keith  and
      Herrmannova, Drahomira  and
      Knoth, Petr  and
      Lo, Kyle  and
      Mayr, Philipp  and
      Patton, Robert M.  and
      Shmueli-Scheuer, Michal  and
      de Waard, Anita  and
      Wang, Kuansan  and
      Wang, Lucy Lu",
    booktitle = "Proceedings of the Second Workshop on Scholarly Document Processing",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.sdp-1.15/",
    doi = "10.18653/v1/2021.sdp-1.15",
    pages = "116--123",
    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 {\ensuremath{<}}claim, title of the abstract{\ensuremath{>}} and continued to train it to select rationales out of each retrieved abstract based on {\ensuremath{<}}claim, sentence{\ensuremath{>}}. 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."
}