Abstract
In recent years, there has been a vast increase in the available clinical data. Variant Deep learning techniques are used to enhance the retrieval and interpretation of these data. This task deployed Natural language inference (NLI) in Clinical Trial Reports (CTRs) to provide individualized care that is supported by evidence. A collection of breast cancer clinical trial records, statements, annotations, and labels from experienced domain experts. NLI presents a chance to advance the widespread understanding and retrieval of medical evidence, leading to significant improvements in connecting the most recent evidence to personalized care. The primary objective is to identify the inference relationship (entailment or contradiction) between pairs of clinical trial records and statements. In this research, we used different transformer-based models, and The proposed model, “Role-based Double Roberta-Large,” achieved the best result on the testing dataset with F1-score equal to 67.0%- Anthology ID:
- 2023.semeval-1.61
- 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:
- 447–452
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.61
- DOI:
- 10.18653/v1/2023.semeval-1.61
- Cite (ACL):
- Kefah Alissa and Malak Abdullah. 2023. JUST-KM at SemEval-2023 Task 7: Multi-evidence Natural Language Inference using Role-based Double Roberta-Large. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 447–452, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- JUST-KM at SemEval-2023 Task 7: Multi-evidence Natural Language Inference using Role-based Double Roberta-Large (Alissa & Abdullah, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.semeval-1.61.pdf