Question-type Driven Question Generation

Wenjie Zhou, Minghua Zhang, Yunfang Wu


Abstract
Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type how while the answer is a personal name. We propose to automatically predict the question type based on the input answer and context. Then, the question type is fused into a seq2seq model to guide the question generation, so as to deal with the mismatching problem. We achieve significant improvement on the accuracy of question type prediction and finally obtain state-of-the-art results for question generation on both SQuAD and MARCO datasets.
Anthology ID:
D19-1622
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6032–6037
Language:
URL:
https://aclanthology.org/D19-1622
DOI:
10.18653/v1/D19-1622
Bibkey:
Cite (ACL):
Wenjie Zhou, Minghua Zhang, and Yunfang Wu. 2019. Question-type Driven Question Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6032–6037, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Question-type Driven Question Generation (Zhou et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1622.pdf