Memory Attention Neural Network for Multi-domain Dialogue State Tracking

摘要：

　　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.

作者: Zihan Xu Zhi Chen Lu Chen Su Zhu Kai Yu

作者单位: MoE Key Lab of Artificial Intelligence,AI Institute,Shanghai Jiao Tong University,Shanghai,China;SpeechLab,Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai,China

会议类型: 国际会议

会议名称: 9th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC 2020)

会议地点: 郑州

会议语种:英文

页码: 41-52

在线出版日期: 2020-10-14（万方平台首次上网日期，不代表论文的发表时间）