计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 212-216.doi: 10.11896/jsjkx.200700137
王博宇, 王中卿, 周国栋
WANG Bo-yu, WANG Zhong-qing, ZHOU Guo-dong
摘要： 随着人机对话系统的不断发展,让计算机能够准确理解对话者的对话意图,并根据对话的历史信息对回复进行意图预测,对于人机对话系统有着十分重要的意义。已有研究重点关注根据对话文本和已有标签对回复进行意图预测,但是,在很多场景下回复可能并没有生成。因此,文中提出了一种结合回复生成的对话意图预测模型。在生成部分,使用Seq2Seq结构,根据对话历史信息生成文本,作为对话中未来回复的文本信息;在分类部分,利用LSTM模型,将生成的回复文本与已有的对话信息转变为子句级别的表示,并结合注意力机制突出同一轮次对话句与生成回复的联系。实验结果表明,所提出的模型相比简单基线模型取得了2.54%的F1-score提升,并且联合训练的方式有助于提升模型性能。
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