NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model

Lung-Hao Lee, Yi Lu, Po-Han Chen, Po-Lei Lee, Kuo-Kai Shyu


Abstract
This study describes the model design of the NCUEE system for the MEDIQA challenge at the ACL-BioNLP 2019 workshop. We use the BERT (Bidirectional Encoder Representations from Transformers) as the word embedding method to integrate the BiLSTM (Bidirectional Long Short-Term Memory) network with an attention mechanism for medical text inferences. A total of 42 teams participated in natural language inference task at MEDIQA 2019. Our best accuracy score of 0.84 ranked the top-third among all submissions in the leaderboard.
Anthology ID:
W19-5058
Volume:
Proceedings of the 18th BioNLP Workshop and Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
528–532
Language:
URL:
https://aclanthology.org/W19-5058
DOI:
10.18653/v1/W19-5058
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W19-5058.pdf