基于语义特征提取与层次结构的问题生成方法

针对传统端到端模型在输入文本语义较复杂情况下生成的问题普遍存在语义不完整的情形，提出一种基于语义特征提取的文本编码器架构.首先构建双向长短时记忆网络获得基础的上下文信息，然后采用自注意力机制及双向卷积神经网络模型分别提取语义的全局特征和局部特征，最后设计一种层次结构，融合特征及输入自身信息得到最终的文本表示进行问题生成.在数据集SQuAD上的实验结果表明，基于语义特征提取与层次结构进行问题生成效果显著，结果明显优于已有方法，并且语义特征提取和层次结构在任务的各评价指标上均有提升.

Question Generation Method Based on Semantic Feature Extraction and Hierarchical Structure

Aiming at  the problem that when the semantics of the input text were relatively complex, traditional end-to-end models generated questions with incomplete semantics,  we proposed a text encoder architecture based on semantic feature extraction. Firstly, we constructed bidirectional long short-term memory network to obtain  basic contextual information. Secondly,  self-attention mechanism was used to extract global features of semantics and bidirectional convolutional neural network model was used to extract local features of semantics respectively. Finally, we designed a hierarchical structure to merge the features and input their own information  to obtain the final text representation for question generation. Experimental results on the SQuAD dataset show that the question generation based on semantic feature extraction and hierarchical structure are significantly effective, and the results are better than the existing methods. Moreover, semantic feature extraction and hierarchical structure are improved  in each evaluation index of the task.