@inproceedings{zhou-etal-2019-multi,
title = "Multi-Task Learning with Language Modeling for Question Generation",
author = "Zhou, Wenjie and
Zhang, Minghua and
Wu, Yunfang",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1337",
doi = "10.18653/v1/D19-1337",
pages = "3394--3399",
abstract = "This paper explores the task of answer-aware questions generation. Based on the attention-based pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions. On both SQuAD and MARCO datasets, our multi-task learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions.",
}
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<abstract>This paper explores the task of answer-aware questions generation. Based on the attention-based pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions. On both SQuAD and MARCO datasets, our multi-task learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions.</abstract>
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%0 Conference Proceedings
%T Multi-Task Learning with Language Modeling for Question Generation
%A Zhou, Wenjie
%A Zhang, Minghua
%A Wu, Yunfang
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhou-etal-2019-multi
%X This paper explores the task of answer-aware questions generation. Based on the attention-based pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a better representation of the input sequence, which will guide the decoder to generate more coherent and fluent questions. On both SQuAD and MARCO datasets, our multi-task learning model boosts the performance, achieving state-of-the-art results. Moreover, human evaluation further proves the high quality of our generated questions.
%R 10.18653/v1/D19-1337
%U https://aclanthology.org/D19-1337
%U https://doi.org/10.18653/v1/D19-1337
%P 3394-3399
Markdown (Informal)
[Multi-Task Learning with Language Modeling for Question Generation](https://aclanthology.org/D19-1337) (Zhou et al., EMNLP-IJCNLP 2019)
ACL
- Wenjie Zhou, Minghua Zhang, and Yunfang Wu. 2019. Multi-Task Learning with Language Modeling for Question Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3394–3399, Hong Kong, China. Association for Computational Linguistics.