KMc-ToD: Structure knowledge enhanced multi-copy network for task-oriented dialogue system

Abstract

Task-oriented dialogue (ToD) system aims to assist users in completing various tasks, which has attracted great interest from researchers. However, the current models introduce delexicalization prepossessing to improve the generalization ability of the models, which makes it difficult to integrate dialogue slots into response generation, resulting in unsatisfactory performance for users. In this paper, we propose a structure knowledge-enhanced multi-copy network (KMc-ToD) for the ToD system, which uses the multi-copy mechanism to selectively copy dialogue slots from the dialogue history and schema graph into the response. Furthermore, to select the appropriate slots, we design the schema graph that helps the model understand the relation between different slots. Specifically, this graph adjusts the weights between nodes according to different utterances to prevent introducing noise. Experimental results on the MultiWOZ datasets show that our model achieves promising performance.