Papers › A Sequence-to-Sequence Approach to Dialogue State Tracking

A Sequence-to-Sequence Approach to Dialogue State Tracking

18 Nov 2020ACL 2021 5arXiv:2011.09553archive 2025-07-28

Yue Feng, Yang Wang, Hang Li

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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Code

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Tasks

ClassificationDecoderDialogue State TrackingMulti-domain Dialogue State TrackingNatural Language Understanding

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Classification SGD SGD_ss F1 (Seqeval) 2020 #1 of 1 Archive leaderboard report
Dialogue State Tracking Second dialogue state tracking challenge Seq2Seq-DU-w/oSchema Joint 85 #1 of 7 Archive leaderboard report
Dialogue State Tracking Wizard-of-Oz Seq2Seq-DU-w/oSchema Joint 91.2 #2 of 10 Archive leaderboard report

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Methods

DST

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