Abstract
In this paper, we present Biomedical Multi-Task Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge. Bio-MTDNN utilizes “transfer learning” based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related. Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain. Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results.- Anthology ID:
- W19-5052
- Volume:
- Proceedings of the 18th BioNLP Workshop and Shared Task
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 488–492
- Language:
- URL:
- https://aclanthology.org/W19-5052
- DOI:
- 10.18653/v1/W19-5052
- Cite (ACL):
- Sahil Chopra, Ankita Gupta, and Anupama Kaushik. 2019. MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 488–492, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain. (Chopra et al., BioNLP 2019)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W19-5052.pdf