Towards Answer-unaware Conversational Question Generation

Mao Nakanishi, Tetsunori Kobayashi, Yoshihiko Hayashi

Abstract
Conversational question generation is a novel area of NLP research which has a range of potential applications. This paper is first to presents a framework for conversational question generation that is unaware of the corresponding answers. To properly generate a question coherent to the grounding text and the current conversation history, the proposed framework first locates the focus of a question in the text passage, and then identifies the question pattern that leads the sequential generation of the words in a question. The experiments using the CoQA dataset demonstrate that the quality of generated questions greatly improves if the question foci and the question patterns are correctly identified. In addition, it was shown that the question foci, even estimated with a reasonable accuracy, could contribute to the quality improvement. These results established that our research direction may be promising, but at the same time revealed that the identification of question patterns is a challenging issue, and it has to be largely refined to achieve a better quality in the end-to-end automatic question generation.
Anthology ID:
D19-5809
Volume:
Proceedings of the 2nd Workshop on Machine Reading for Question Answering
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, Danqi Chen
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
63–71
Language:
URL:
https://aclanthology.org/D19-5809
DOI:
10.18653/v1/D19-5809
Bibkey:
Cite (ACL):
Mao Nakanishi, Tetsunori Kobayashi, and Yoshihiko Hayashi. 2019. Towards Answer-unaware Conversational Question Generation. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 63–71, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Towards Answer-unaware Conversational Question Generation (Nakanishi et al., 2019)
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