@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://aclanthology.org/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 {\textquotedblleft}NOT{\_}ENOUGH{\_}INFO{\textquotedblright} or {\textquotedblleft}ENOUGH{\_}INFO{\textquotedblright}, then label those marked as {\textquotedblleft}ENOUGH{\_}INFO{\textquotedblright} as either {\textquotedblleft}SUPPORT{\textquotedblright} or {\textquotedblleft}CONTRADICT{\textquotedblright}. 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."
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<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.</abstract>
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%0 Conference Proceedings
%T QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification
%A Zeng, Xia
%A Zubiaga, Arkaitz
%Y Beltagy, Iz
%Y Cohan, Arman
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Hall, Keith
%Y Herrmannova, Drahomira
%Y Knoth, Petr
%Y Lo, Kyle
%Y Mayr, Philipp
%Y Patton, Robert M.
%Y Shmueli-Scheuer, Michal
%Y de Waard, Anita
%Y Wang, Kuansan
%Y Wang, Lucy Lu
%S Proceedings of the Second Workshop on Scholarly Document Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F zeng-zubiaga-2021-qmul
%X 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.
%R 10.18653/v1/2021.sdp-1.15
%U https://aclanthology.org/2021.sdp-1.15/
%U https://doi.org/10.18653/v1/2021.sdp-1.15
%P 116-123
Markdown (Informal)
[QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification](https://aclanthology.org/2021.sdp-1.15/) (Zeng & Zubiaga, sdp 2021)
ACL