Abstract
The “Multi-evidence Natural Language Inference forClinical Trial Data” task at SemEval 2023competition focuses on extracting essentialinformation on clinical trial data, by posing twosubtasks on textual entailment and evidence retrieval. In the context of SemEval, we present a comparisonbetween a method based on the BioBERT model anda CNN model. The task is based on a collection ofbreast cancer Clinical Trial Reports (CTRs),statements, explanations, and labels annotated bydomain expert annotators. We achieved F1 scores of0.69 for determining the inference relation(entailment vs contradiction) between CTR -statement pairs. The implementation of our system ismade available via Github - https://github.com/volosincu/FII_Smart__Semeval2023.- Anthology ID:
- 2023.semeval-1.30
- 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:
- 212–220
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2023.semeval-1.30/
- DOI:
- 10.18653/v1/2023.semeval-1.30
- Cite (ACL):
- Mihai Volosincu, Cosmin Lupu, Diana Trandabat, and Daniela Gifu. 2023. FII SMART at SemEval 2023 Task7: Multi-evidence Natural Language Inference for Clinical Trial Data. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 212–220, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- FII SMART at SemEval 2023 Task7: Multi-evidence Natural Language Inference for Clinical Trial Data (Volosincu et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2023.semeval-1.30.pdf