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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/D19-1622.pdf