Personalized Dialogue System with External Knowledge

Abstract

In this paper, we propose a new personalization method for a dialogue system using external knowledge and Graph Convolutional Networks (GCNs). The proposed system is divided into a personalize-part and a response-generation part with Japanese Transformer model. The personalize-part consists of a learned GCN, which can estimate the user’s interests, and a knowledge graph as external knowledge. The personalize-part can add an appropriate template to the response by Transformer according to the dialogue situation and user’s interests. Estimating a topic interesting for each user, the new system can provide an intimate dialogue reflecting individualized interests. We carried out extensive evaluation experiments, and it has shown that the proposed system exceeds the conventional Transformer dialogue model by 35.6% in the variety of information and by 34.0% in the level of desire to use the system.