PathQG: Neural Question Generation from Facts
Siyuan Wang, Zhongyu Wei, Zhihao Fan, Zengfeng Huang, Weijian Sun, Qi Zhang, Xuanjing Huang · 2020
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. 1