@inproceedings{subramanian-etal-2018-neural,
title = "Neural Models for Key Phrase Extraction and Question Generation",
author = "Subramanian, Sandeep and
Wang, Tong and
Yuan, Xingdi and
Zhang, Saizheng and
Trischler, Adam and
Bengio, Yoshua",
editor = "Choi, Eunsol and
Seo, Minjoon and
Chen, Danqi and
Jia, Robin and
Berant, Jonathan",
booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2609",
doi = "10.18653/v1/W18-2609",
pages = "78--88",
abstract = "We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.",
}
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<abstract>We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.</abstract>
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%0 Conference Proceedings
%T Neural Models for Key Phrase Extraction and Question Generation
%A Subramanian, Sandeep
%A Wang, Tong
%A Yuan, Xingdi
%A Zhang, Saizheng
%A Trischler, Adam
%A Bengio, Yoshua
%Y Choi, Eunsol
%Y Seo, Minjoon
%Y Chen, Danqi
%Y Jia, Robin
%Y Berant, Jonathan
%S Proceedings of the Workshop on Machine Reading for Question Answering
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F subramanian-etal-2018-neural
%X We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.
%R 10.18653/v1/W18-2609
%U https://aclanthology.org/W18-2609
%U https://doi.org/10.18653/v1/W18-2609
%P 78-88
Markdown (Informal)
[Neural Models for Key Phrase Extraction and Question Generation](https://aclanthology.org/W18-2609) (Subramanian et al., ACL 2018)
ACL
- Sandeep Subramanian, Tong Wang, Xingdi Yuan, Saizheng Zhang, Adam Trischler, and Yoshua Bengio. 2018. Neural Models for Key Phrase Extraction and Question Generation. In Proceedings of the Workshop on Machine Reading for Question Answering, pages 78–88, Melbourne, Australia. Association for Computational Linguistics.