@inproceedings{li-etal-2019-net,
title = "{D}-{NET}: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension",
author = "Li, Hongyu and
Zhang, Xiyuan and
Liu, Yibing and
Zhang, Yiming and
Wang, Quan and
Zhou, Xiangyang and
Liu, Jing and
Wu, Hua and
Wang, Haifeng",
editor = "Fisch, Adam and
Talmor, Alon and
Jia, Robin and
Seo, Minjoon and
Choi, Eunsol and
Chen, Danqi",
booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5828",
doi = "10.18653/v1/D19-5828",
pages = "212--219",
abstract = "In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. Our system is built on a framework of pretraining and fine-tuning, namely D-NET. The techniques of pre-trained language models and multi-task learning are explored to improve the generalization of MRC models and we conduct experiments to examine the effectiveness of these strategies. Our system is ranked at top 1 of all the participants in terms of averaged F1 score. Our codes and models will be released at PaddleNLP.",
}
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<abstract>In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. Our system is built on a framework of pretraining and fine-tuning, namely D-NET. The techniques of pre-trained language models and multi-task learning are explored to improve the generalization of MRC models and we conduct experiments to examine the effectiveness of these strategies. Our system is ranked at top 1 of all the participants in terms of averaged F1 score. Our codes and models will be released at PaddleNLP.</abstract>
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%0 Conference Proceedings
%T D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension
%A Li, Hongyu
%A Zhang, Xiyuan
%A Liu, Yibing
%A Zhang, Yiming
%A Wang, Quan
%A Zhou, Xiangyang
%A Liu, Jing
%A Wu, Hua
%A Wang, Haifeng
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F li-etal-2019-net
%X In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models. Our system is built on a framework of pretraining and fine-tuning, namely D-NET. The techniques of pre-trained language models and multi-task learning are explored to improve the generalization of MRC models and we conduct experiments to examine the effectiveness of these strategies. Our system is ranked at top 1 of all the participants in terms of averaged F1 score. Our codes and models will be released at PaddleNLP.
%R 10.18653/v1/D19-5828
%U https://aclanthology.org/D19-5828
%U https://doi.org/10.18653/v1/D19-5828
%P 212-219
Markdown (Informal)
[D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension](https://aclanthology.org/D19-5828) (Li et al., 2019)
ACL
- Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, and Haifeng Wang. 2019. D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 212–219, Hong Kong, China. Association for Computational Linguistics.