Dynamic Network Model for Multi-Domain End-to-end Task-Oriented Dialogue System

Abstract

Dialogue State Tracking (DST) is an important part in task-oriented dialog system, whose target is to infer the current dialog states and user intentions according to the dialog history information. To this end, we have achieved improvements to the existing work and proposed a dynamic network model suitable for multi-domain dialog, which can explicitly use domain information and better cope with zero-shot tasks. The model is composed of three modules: an encoder, a decoder and a slot classifier. The encoder module introduces a mixed-separate framework so that it can obtain the feature information of each domain on the premise of extracting the shared information between all domains. The experimental results show that the model achieves joint accuracy of 48.38% for the five domains of MultiWOZ, which is superior to existing models. Besides, by simulating the zero-shot scenario, the knowledge transferability of the model has also been well proven. Finally, in order to verify the effectiveness of the robot simulation system, this paper also uses the robot simulation technology to simulate the common tasks of helping users complete the task of taking items in the home environment service.