Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model

Jiin Nam, Seunghyun Yoon, Kyomin Jung


Abstract
While deep learning techniques have shown promising results in many natural language processing (NLP) tasks, it has not been widely applied to the clinical domain. The lack of large datasets and the pervasive use of domain-specific language (i.e. abbreviations and acronyms) in the clinical domain causes slower progress in NLP tasks than that of the general NLP tasks. To fill this gap, we employ word/subword-level based models that adopt large-scale data-driven methods such as pre-trained language models and transfer learning in analyzing text for the clinical domain. Empirical results demonstrate the superiority of the proposed methods by achieving 90.6% accuracy in medical domain natural language inference task. Furthermore, we inspect the independent strengths of the proposed approaches in quantitative and qualitative manners. This analysis will help researchers to select necessary components in building models for the medical domain.
Anthology ID:
W19-5043
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:
406–414
Language:
URL:
https://aclanthology.org/W19-5043
DOI:
10.18653/v1/W19-5043
Bibkey:
Cite (ACL):
Jiin Nam, Seunghyun Yoon, and Kyomin Jung. 2019. Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 406–414, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Surf at MEDIQA 2019: Improving Performance of Natural Language Inference in the Clinical Domain by Adopting Pre-trained Language Model (Nam et al., BioNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5043.pdf
Data
MultiNLISNLI