@inproceedings{449857d5167f4f93aa20355f4721f3b2,
title = "NCUEE at MEDIQA 2019: Medical text inference using ensemble BERT-BiLSTM-attention model",
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.",
author = "Lee, {Lung Hao} and Yi Lu and Chen, {Po Han} and Lee, {Po Lei} and Shyu, {Kuo Kai}",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computational Linguistics; 18th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2019 ; Conference date: 01-08-2019",
year = "2019",
language = "English",
series = "BioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task",
publisher = "Association for Computational Linguistics (ACL)",
pages = "528--532",
booktitle = "BioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task",
address = "美國",
}