DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering
Huiwei Zhou, Xuefei Li, Weihong Yao, Chengkun Lang, Shixian Ning
Abstract
In this paper, we propose a novel model called Adversarial Multi-Task Network (AMTN) for jointly modeling Recognizing Question Entailment (RQE) and medical Question Answering (QA) tasks. AMTN utilizes a pre-trained BioBERT model and an Interactive Transformer to learn the shared semantic representations across different task through parameter sharing mechanism. Meanwhile, an adversarial training strategy is introduced to separate the private features of each task from the shared representations. Experiments on BioNLP 2019 RQE and QA Shared Task datasets show that our model benefits from the shared representations of both tasks provided by multi-task learning and adversarial training, and obtains significant improvements upon the single-task models.- Anthology ID:
- W19-5046
- Volume:
- Proceedings of the 18th BioNLP Workshop and Shared Task
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 437–445
- Language:
- URL:
- https://aclanthology.org/W19-5046
- DOI:
- 10.18653/v1/W19-5046
- Cite (ACL):
- Huiwei Zhou, Xuefei Li, Weihong Yao, Chengkun Lang, and Shixian Ning. 2019. DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 437–445, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering (Zhou et al., BioNLP 2019)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W19-5046.pdf