Abstract: Conversational question generation (CQG) is different from the question generation task of generating single-round questions based on paragraphs and answers.CQG additionally considers the conversational information composed of historical question and answer pairs,and the generated questions inherit the historical content of the conversation and maintain high consistency.In response to this feature,the article proposes word-level and sentence-level attention mechanism modules to enhance the ability to extract conversation history information,ensuring that the current round of questions integrates the characteristics of each word and sentence in the conversation history,thereby generating a coherent,high-quality question.The accuracy of the question word is more important.The generated question needs to match the answer type corresponding to the original question in the data set.An additional loss function is constructed in the question word prediction module as a limitation of the question word type.The conversational comprehension network (CCNet) model is obtained by synthesizing each module.Experiments show that this model is higher than the baseline model in most evaluation indicators.On the CoQA dataset,Bleu1 and Bleu2 reach 39.70 and 23.76,respectively,and the quality of the generated questions is higher.The model is proved to be effective in ablation experiments and cross-dataset experiments,indicating that the CCNet model has strong general capabilities.

Key words: Attention mechanism, Conversational question generation, Gated network, Question generation, Recurrent neural network