Memory Attention Neural Network for Multi-domain Dialogue State Tracking

Abstract

In a task-oriented dialogue system, the dialogue state tracker aims to generate a structured summary (domain-slot-value triples) over the whole dialogue utterance. However, existing approaches generally fail to make good use of pre-defined ontologies. In this paper, we propose a novel Memory Attention State Tracker that considers ontologies as prior knowledge and utilizes Memory Network to store such information. Our model is composed of an utterance encoder, an attention-based query generator, a slot gate classifier, and ontology Memory Networks for every domain-slot pair. To make a fair comparison with previous approaches, we also conduct experiments with RNN instead of pre-trained BERT as the encoder. Empirical results show that our model achieves a compatible joint accuracy on MultiWoz 2.0 dataset and MultiWoz 2.1 dataset.