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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

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.

Original languageEnglish
Title of host publicationBioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task
PublisherAssociation for Computational Linguistics (ACL)
Pages528-532
Number of pages5
ISBN (Electronic)9781950737284
StatePublished - 2019
Event18th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2019 - Florence, Italy
Duration: 1 Aug 2019 → …

Publication series

NameBioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task

Conference

Conference18th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2019
Country/TerritoryItaly
CityFlorence
Period1/08/19 → …

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