This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system. Significant progress has been achieved recently on the development of DST technologies. However, building a DST module that is scalable and effective is still a challenging issue. This paper proposes a new approach to dialogue state tracking, referred to as Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. It employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue. Seq2Seq-DU has the following advantages. It can jointly model the relations between intents, slots, and slot values; it can utilize rich language representations of utterances and schemas; it can effectively deal with categorical slots, non-categorical slots, and unseen schemas. In addition, Seq2Seq-DU can also be used in the NLU (natural language understanding) module of a dialogue system. Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2, WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.

该研究论文提出了一种新的对话状态追踪方法，使用Seq2Seq-DU，该方法通过两个基于BERT的编码器来编码对话中的话语和模式的描述，可以有效地处理类别和非类别槽，并能够联合建模意图、槽和槽值，其效果较现有方法更为优越。

一个面向对话状态追踪的序列转序列方法