基于图卷积网络的知识感知对话生成

引入结构化知识的对话系统因为能够生成流畅度更高、多样性更丰富的对话回复而受到广泛关注, 但是以往的研究只注重于结构化知识中的实体, 却忽略了实体之间的关系以及知识的完整性. 本文提出了一种基于图卷积网络的知识感知对话生成模型(KCG). 该模型通过知识编码器分别捕获实体与关系的语义信息并利用图卷积网络增强实体表征; 再利用知识选择模块获得与对话上下文相关的实体与关系的知识选择概率分布; 最后将知识选择概率分布与词表概率分布融合, 解码器以此选择知识或词表字词. 本文在中文公开数据集DuConv上进行实验, 结果表明, KCG在自动评估指标上优于目前的基线模型, 能生成更加流畅并且内容更加丰富的回复.

Knowledge-aware Conversation Generation Based on Graph Convolutional Network

The dialogue system that introduces structured knowledge has attracted widespread attention as it can generate more fluent and diverse dialogue replies. However, previous studies only focus on entities in structured knowledge, ignoring the relation between entities and the integrity of knowledge. In this study, a knowledge-aware conversation generation (KCG) model based on the graph convolutional network is proposed. The semantic information of the entity and relation is captured by the knowledge encoder and the representation of the entity is enhanced by the graph convolutional network. Then, the knowledge selection module is applied to obtain the knowledge selection probability distribution of the entities and relations related to the dialogue context. Finally, the knowledge selection probability distribution is fused with the vocabulary probability distribution so that the decoder can select the knowledge or words. In this study, the experiments are conducted on DuConv, a Chinese public data set. The results show that KCG is superior to the current baseline model in terms of automatic evaluation metrics and can generate more fluent and informative replies.