@inproceedings{duan-etal-2017-question,
title = "Question Generation for Question Answering",
author = "Duan, Nan and
Tang, Duyu and
Chen, Peng and
Zhou, Ming",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1090",
doi = "10.18653/v1/D17-1090",
pages = "866--874",
abstract = "This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data. The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one is a retrieval-based method using convolution neural network (CNN), the other is a generation-based method using recurrent neural network (RNN); Second, we show how to leverage the generated questions to improve existing question answering systems. We evaluate our question generation method for the answer sentence selection task on three benchmark datasets, including SQuAD, MS MARCO, and WikiQA. Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.",
}
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%0 Conference Proceedings
%T Question Generation for Question Answering
%A Duan, Nan
%A Tang, Duyu
%A Chen, Peng
%A Zhou, Ming
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F duan-etal-2017-question
%X This paper presents how to generate questions from given passages using neural networks, where large scale QA pairs are automatically crawled and processed from Community-QA website, and used as training data. The contribution of the paper is 2-fold: First, two types of question generation approaches are proposed, one is a retrieval-based method using convolution neural network (CNN), the other is a generation-based method using recurrent neural network (RNN); Second, we show how to leverage the generated questions to improve existing question answering systems. We evaluate our question generation method for the answer sentence selection task on three benchmark datasets, including SQuAD, MS MARCO, and WikiQA. Experimental results show that, by using generated questions as an extra signal, significant QA improvement can be achieved.
%R 10.18653/v1/D17-1090
%U https://aclanthology.org/D17-1090
%U https://doi.org/10.18653/v1/D17-1090
%P 866-874
Markdown (Informal)
[Question Generation for Question Answering](https://aclanthology.org/D17-1090) (Duan et al., EMNLP 2017)
ACL
- Nan Duan, Duyu Tang, Peng Chen, and Ming Zhou. 2017. Question Generation for Question Answering. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 866–874, Copenhagen, Denmark. Association for Computational Linguistics.