A Sequence-to-Sequence Approach to Dialogue State Tracking

• URL: http://arxiv.org/abs/2011.09553v2
• Date: Sat, 29 May 2021 02:48:28 GMT
• Title: A Sequence-to-Sequence Approach to Dialogue State Tracking
• Authors: Yue Feng, Yang Wang, Hang Li
• Abstract summary: Seq2Seq-DU formalizes dialogue state tracking as a sequence-to-sequence problem. It can jointly model intents, slots, and slot values. It can effectively deal with categorical and non-categorical slots, and unseen schemas.
• Score: 17.81139775400199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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