Knowledge-based Context-aware Multi-turn Conversational Model with Hierarchical Attention

摘要

We study response generation in multi-turn open- domain dialogue systems. Background knowledge based response generation has been developed to make dialogue models generate more informative and appropriate responses. However, these knowledge-based dialogue models are limited to the domain of single round conversation, and fail to consider the role of dialogue context in the selection of relevant knowledge and response generation. As a result, these models might lose some useful information in the dialogue context and generate irrelevant responses. We argue that both dialogue context and relevant knowledge play important roles in the response generation of multiturn open-domain dialogue systems. We propose a Knowledge- based Context-aware Multi-turn Conversational (KCMC) model to consider both dialogue context and relevant knowledge in a unified framework. The Knowledge Fusion module is designed to augment the semantic representation of dialogue context with associated knowledge triples. And we introduce hierarchical encoders to model the hierarchy of dialogue context and to capture important information in the dialogue context. Furthermore, a hierarchical attention mechanism attends to important parts of knowledge triples, which facilitates better knowledge selection and response generation. Through extensive experiments on two datasets, we demonstrate that the proposed model is capable of generating more informative and appropriate responses than baseline models.