  This paper is concerned with dialogue state tracking (DST) in a task-oriented
dialogue system. Building a DST module that is highly effective is still a
challenging issue, although significant progresses have been made recently.
This paper proposes a new approach to dialogue state tracking, referred to as
Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. Seq2Seq-DU
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 intents, slots, and slot values;
it can leverage the rich representations of utterances and schemas based on
BERT; it can effectively deal with categorical and 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, MultiWOZ2.1,
WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the
existing methods.
