Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment

Sai Abishek Bhaskar, Rashi Rungta, James Route, Eric Nyberg, Teruko Mitamura


Abstract
This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH. We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations. Our final models for the NLI and RQE tasks achieve the 4th and 2nd rank on the shared-task leaderboard respectively.
Anthology ID:
W19-5049
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:
462–470
Language:
URL:
https://aclanthology.org/W19-5049
DOI:
10.18653/v1/W19-5049
Bibkey:
Cite (ACL):
Sai Abishek Bhaskar, Rashi Rungta, James Route, Eric Nyberg, and Teruko Mitamura. 2019. Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 462–470, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment (Bhaskar et al., BioNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5049.pdf
Data
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