PathQG: Neural Question Generation from Facts

Siyuan Wang, Zhongyu Wei, Zhihao Fan, Zengfeng Huang, Weijian Sun, Qi Zhang, Xuanjing Huang


Abstract
Existing research for question generation encodes the input text as a sequence of tokens without explicitly modeling fact information. These models tend to generate irrelevant and uninformative questions. In this paper, we explore to incorporate facts in the text for question generation in a comprehensive way. We present a novel task of question generation given a query path in the knowledge graph constructed from the input text. We divide the task into two steps, namely, query representation learning and query-based question generation. We formulate query representation learning as a sequence labeling problem for identifying the involved facts to form a query and employ an RNN-based generator for question generation. We first train the two modules jointly in an end-to-end fashion, and further enforce the interaction between these two modules in a variational framework. We construct the experimental datasets on top of SQuAD and results show that our model outperforms other state-of-the-art approaches, and the performance margin is larger when target questions are complex. Human evaluation also proves that our model is able to generate relevant and informative questions.
Anthology ID:
2020.emnlp-main.729
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9066–9075
Language:
URL:
https://aclanthology.org/2020.emnlp-main.729
DOI:
10.18653/v1/2020.emnlp-main.729
Bibkey:
Cite (ACL):
Siyuan Wang, Zhongyu Wei, Zhihao Fan, Zengfeng Huang, Weijian Sun, Qi Zhang, and Xuanjing Huang. 2020. PathQG: Neural Question Generation from Facts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9066–9075, Online. Association for Computational Linguistics.
Cite (Informal):
PathQG: Neural Question Generation from Facts (Wang et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.729.pdf
Optional supplementary material:
 2020.emnlp-main.729.OptionalSupplementaryMaterial.zip
Video:
 https://slideslive.com/38939259
Code
 WangsyGit/PathQG