ITTC at SemEval 2023-Task 7: Document Retrieval and Sentence Similarity for Evidence Retrieval in Clinical Trial Data

Rahmad Mahendra, Damiano Spina, Karin Verspoor

Abstract
This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the SemEval 2023 Task 7, i.e., multi-evidence natural language inference for clinical trial data (NLI4CT). More specifically, we were working on subtask 2 whose objective is to identify the relevant parts of the premise from clinical trial report that justify the truth of information in the statement. We approach the evidence retrieval problem as a document retrieval and sentence similarity task. Our results show that the task poses some challenges which involve dealing with complex sentences and implicit evidences.
Anthology ID:
2023.semeval-1.316
Volume:
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
2338–2342
Language:
URL:
https://aclanthology.org/2023.semeval-1.316
DOI:
10.18653/v1/2023.semeval-1.316
Bibkey:
Cite (ACL):
Rahmad Mahendra, Damiano Spina, and Karin Verspoor. 2023. ITTC at SemEval 2023-Task 7: Document Retrieval and Sentence Similarity for Evidence Retrieval in Clinical Trial Data. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2338–2342, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
ITTC at SemEval 2023-Task 7: Document Retrieval and Sentence Similarity for Evidence Retrieval in Clinical Trial Data (Mahendra et al., SemEval 2023)
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https://preview.aclanthology.org/naacl24-info/2023.semeval-1.316.pdf
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