Neural Models for Key Phrase Extraction and Question Generation

Sandeep Subramanian, Tong Wang, Xingdi Yuan, Saizheng Zhang, Adam Trischler, Yoshua Bengio


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.
Anthology ID:
W18-2609
Volume:
Proceedings of the Workshop on Machine Reading for Question Answering
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Eunsol Choi, Minjoon Seo, Danqi Chen, Robin Jia, Jonathan Berant
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
78–88
Language:
URL:
https://aclanthology.org/W18-2609
DOI:
10.18653/v1/W18-2609
Bibkey:
Cite (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.
Cite (Informal):
Neural Models for Key Phrase Extraction and Question Generation (Subramanian et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-2609.pdf
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