# A Sequence-to-Sequence Approach to Dialogue State Tracking

arXiv: https://arxiv.org/abs/2011.09553 · HTML: https://mlsanity.com/paper/2011.09553/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 30 of 43)

- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [Dialogue State Tracking with a Language Model using Schema-Driven Prompting](https://mlsanity.com/paper/2109.07506/) (2021)
- [Are LLMs All You Need for Task-Oriented Dialogue?](https://mlsanity.com/paper/2304.06556/) (2023)
- [SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation](https://mlsanity.com/paper/2209.06664/) (2022)
- [Learning to Execute Actions or Ask Clarification Questions](https://mlsanity.com/paper/2204.08373/) (2022)
- [Towards LLM-driven Dialogue State Tracking](https://mlsanity.com/paper/2310.14970/) (2023)
- ["Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking](https://mlsanity.com/paper/2207.14627/) (2022)
- [Amendable Generation for Dialogue State Tracking](https://mlsanity.com/paper/2110.15659/) (2021)
- [Effective Sequence-to-Sequence Dialogue State Tracking](https://mlsanity.com/paper/2108.13990/) (2021)
- [Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2204.06677/) (2022)
- [Show, Don't Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue](https://mlsanity.com/paper/2204.04327/) (2022)
- [ASSIST: Towards Label Noise-Robust Dialogue State Tracking](https://mlsanity.com/paper/2202.13024/) (2022)
- [Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues](https://mlsanity.com/paper/2305.16798/) (2023)
- [Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation](https://mlsanity.com/paper/2205.13346/) (2022)
- [Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues](https://mlsanity.com/paper/2305.13690/) (2023)
- [Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation](https://mlsanity.com/paper/2306.04724/) (2023)
- [LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking](https://mlsanity.com/paper/2205.02550/) (2022)
- [Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking](https://mlsanity.com/paper/2205.10059/) (2022)
- [PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts](https://mlsanity.com/paper/2306.04535/) (2023)
- [On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools](https://mlsanity.com/paper/2202.07156/) (2022)
- [Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking](https://mlsanity.com/paper/2203.03123/) (2022)
- [Diable: Efficient Dialogue State Tracking as Operations on Tables](https://mlsanity.com/paper/2305.17020/) (2023)
- [MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking](https://mlsanity.com/paper/2211.05503/) (2022)
- [Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access](https://mlsanity.com/paper/2212.05373/) (2022)
- [Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema](https://mlsanity.com/paper/2303.16252/) (2023)
- [Chain of Thought Explanation for Dialogue State Tracking](https://mlsanity.com/paper/2403.04656/) (2024)
- [What Did You Say? Task-Oriented Dialog Datasets Are Not Conversational!?](https://mlsanity.com/paper/2203.03431/) (2022)
- [Prompt-based Generative Approach towards Multi-Hierarchical Medical Dialogue State Tracking](https://mlsanity.com/paper/2203.09946/) (2022)
- [Toward Open-domain Slot Filling via Self-supervised Co-training](https://mlsanity.com/paper/2303.13801/) (2023)
- [Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking](https://mlsanity.com/paper/2306.09340/) (2023)

## Abstract (arXiv)

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.