Neural Question Generation using Interrogative Phrases

Yuichi Sasazawa, Sho Takase, Naoaki Okazaki


Abstract
Question Generation (QG) is the task of generating questions from a given passage. One of the key requirements of QG is to generate a question such that it results in a target answer. Previous works used a target answer to obtain a desired question. However, we also want to specify how to ask questions and improve the quality of generated questions. In this study, we explore the use of interrogative phrases as additional sources to control QG. By providing interrogative phrases, we expect that QG can generate a more reliable sequence of words subsequent to an interrogative phrase. We present a baseline sequence-to-sequence model with the attention, copy, and coverage mechanisms, and show that the simple baseline achieves state-of-the-art performance. The experiments demonstrate that interrogative phrases contribute to improving the performance of QG. In addition, we report the superiority of using interrogative phrases in human evaluation. Finally, we show that a question answering system can provide target answers more correctly when the questions are generated with interrogative phrases.
Anthology ID:
W19-8613
Volume:
Proceedings of the 12th International Conference on Natural Language Generation
Month:
October–November
Year:
2019
Address:
Tokyo, Japan
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–111
Language:
URL:
https://aclanthology.org/W19-8613
DOI:
10.18653/v1/W19-8613
Bibkey:
Cite (ACL):
Yuichi Sasazawa, Sho Takase, and Naoaki Okazaki. 2019. Neural Question Generation using Interrogative Phrases. In Proceedings of the 12th International Conference on Natural Language Generation, pages 106–111, Tokyo, Japan. Association for Computational Linguistics.
Cite (Informal):
Neural Question Generation using Interrogative Phrases (Sasazawa et al., INLG 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-8613.pdf