Question Generation Model Based on the Answer and Its Contexts
TAN Hongye1,2, SUN Xiuqin1, YAN Zhen1
1.School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China; 2.Key Laboratory of Intelligence and Chinese Information Processing, Ministry of Education, Shanxi University, Taiyuan, Shanxi 030006, China
Abstract：Text-based question generation is to generate related questions from a given sentence or paragraph. In recent years, sequence-to-sequence neural network models have been used to generate questions for sentences containing answers. However, these methods have the limitations: (1) the generated interrogatives do not match the answer type; and (2) the relevance of questions and the answer is not strong. This paper proposes a question generation model that based on answers and the contextual information. The model first determines interrogatives that match the answer type according to the relationship between the answer and the context information. Then, the model uses the answer and the context information to determine words related to questions, so that questions use words in the original text as much as possible. Finally, the model combines answer features, interrogatives, words related to questions with original sentences as inputs to generate a question. Experiments show that the proposed model is significantly better than the baseline systems.
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