PathQG: Neural Question Generation from Facts

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 * Corresponding author 1 Our code is available at https://github.com/ WangsyGit/PathQG . (a) Machine generated questions (Qs) for an input text together with human generated ones (GTQs). Phrases underlined are the answers to ground-truth questions. (b) Knowledge graph constructed based on the input text shown in top sub-figure. Two colored ellipsoid are two query paths related to two ground truth questions in sub-figure (a) respectively. Nodes in green are covered by ground-truth questions.