基于答案及其上下文信息的问题生成模型

基于文本的问题生成是从给定的句子或段落中生成相关问题。目前,主要采用序列到序列的神经网络模型来研究包含答案的句子生成问题,然而这些方法存在以下问题: ①生成的疑问词与答案类型不匹配; ②问题与答案的相关性不强。该文提出一个基于答案及其上下文信息的问题生成模型。该模型首先根据答案与上下文信息的关系确定与答案类型匹配的疑问词;然后利用答案及其上下文信息确定问题相关词,使问题尽可能使用原文中的词;最后结合原句作为输入来生成问题。相关实验表明,该文提出的模型性能明显优于基线系统。

Question Generation Model Based on the Answer and Its Contexts

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.