计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 232-238.doi: 10.11896/jsjkx.210200153
时雨涛, 孙晓
SHI Yu-tao, SUN Xiao
摘要： 会话问题生成(Conversational Question Generation,CQG)不同于根据段落和答案生成单轮问题的问题生成任务,CQG额外考虑由历史问答对构成的会话信息,生成的问题承接会话历史内容,保持较高的一致性。针对这一特性,文中提出了字级别和句级别注意力机制模块来增强对会话历史信息的提取能力,确保当前轮次的问题融合会话历史中每个词和句子的特征,从而生成连贯的、高质量的问题。疑问词的正确性较重要,生成的问题需要和数据集中原始问题对应的答案类型相互匹配,在疑问词预测模块中构造额外的损失函数作为疑问词类型的限制。综合各个模块得到会话理解模型(Conversational Comprehension Network,CCNet),实验结果表明,该模型在大部分评测指标上高于基线模型,在CoQA数据集上Bleu1和Bleu2分别达到39.70和23.76,生成的问题质量更高。在消融实验和跨数据集实验中该模型被证明是有效的,说明CCNet模型具有较强的通用能力。
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