@inproceedings{xu-etal-2019-doubletransfer,
title = "{D}ouble{T}ransfer at {MEDIQA} 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain",
author = "Xu, Yichong and
Liu, Xiaodong and
Li, Chunyuan and
Poon, Hoifung and
Gao, Jianfeng",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5042",
doi = "10.18653/v1/W19-5042",
pages = "399--405",
abstract = "This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain. For transfer learning fine-tuning, we use multi-task learning on NLI, RQE and QA tasks on general and medical domains to improve performance. The proposed methods are proved effective for natural language understanding in the medical domain, and we rank the first place on the QA task.",
}
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<abstract>This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain. For transfer learning fine-tuning, we use multi-task learning on NLI, RQE and QA tasks on general and medical domains to improve performance. The proposed methods are proved effective for natural language understanding in the medical domain, and we rank the first place on the QA task.</abstract>
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%0 Conference Proceedings
%T DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
%A Xu, Yichong
%A Liu, Xiaodong
%A Li, Chunyuan
%A Poon, Hoifung
%A Gao, Jianfeng
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F xu-etal-2019-doubletransfer
%X This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain. For transfer learning fine-tuning, we use multi-task learning on NLI, RQE and QA tasks on general and medical domains to improve performance. The proposed methods are proved effective for natural language understanding in the medical domain, and we rank the first place on the QA task.
%R 10.18653/v1/W19-5042
%U https://aclanthology.org/W19-5042
%U https://doi.org/10.18653/v1/W19-5042
%P 399-405
Markdown (Informal)
[DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain](https://aclanthology.org/W19-5042) (Xu et al., BioNLP 2019)
ACL