PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation

Wei Zhu, Xiaofeng Zhou, Keqiang Wang, Xun Luo, Xiepeng Li, Yuan Ni, Guotong Xie


Abstract
This paper describes the models designated for the MEDIQA 2019 shared tasks by the team PANLP. We take advantages of the recent advances in pre-trained bidirectional transformer language models such as BERT (Devlin et al., 2018) and MT-DNN (Liu et al., 2019b). We find that pre-trained language models can significantly outperform traditional deep learning models. Transfer learning from the NLI task to the RQE task is also experimented, which proves to be useful in improving the results of fine-tuning MT-DNN large. A knowledge distillation process is implemented, to distill the knowledge contained in a set of models and transfer it into an single model, whose performance turns out to be comparable with that obtained by the ensemble of that set of models. Finally, for test submissions, model ensemble and a re-ranking process are implemented to boost the performances. Our models participated in all three tasks and ranked the 1st place for the RQE task, and the 2nd place for the NLI task, and also the 2nd place for the QA task.
Anthology ID:
W19-5040
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:
380–388
Language:
URL:
https://aclanthology.org/W19-5040
DOI:
10.18653/v1/W19-5040
Bibkey:
Cite (ACL):
Wei Zhu, Xiaofeng Zhou, Keqiang Wang, Xun Luo, Xiepeng Li, Yuan Ni, and Guotong Xie. 2019. PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 380–388, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation (Zhu et al., BioNLP 2019)
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PDF:
https://aclanthology.org/W19-5040.pdf