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
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 528–532
- Language:
- URL:
- https://aclanthology.org/W19-5058
- DOI:
- 10.18653/v1/W19-5058
- Cite (ACL):
- Lung-Hao Lee, Yi Lu, Po-Han Chen, Po-Lei Lee, and Kuo-Kai Shyu. 2019. NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 528–532, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model (Lee et al., BioNLP 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W19-5058.pdf