# Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation

arXiv: https://arxiv.org/abs/1909.00754 · HTML: https://mlsanity.com/paper/1909.00754/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 21 of 21)

- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- [Amendable Generation for Dialogue State Tracking](https://mlsanity.com/paper/2110.15659/) (2021)
- [Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking](https://mlsanity.com/paper/2203.01552/) (2022)
- [Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2204.06677/) (2022)
- [Recent Advances in Neural Text Generation: A Task-Agnostic Survey](https://mlsanity.com/paper/2203.03047/) (2022)
- [Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking](https://mlsanity.com/paper/2205.10059/) (2022)
- [Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?](https://mlsanity.com/paper/2105.03571/) (2021)
- [Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking](https://mlsanity.com/paper/2203.03123/) (2022)
- [OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue](https://mlsanity.com/paper/2209.04595/) (2022)
- [Diable: Efficient Dialogue State Tracking as Operations on Tables](https://mlsanity.com/paper/2305.17020/) (2023)
- [OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System](https://mlsanity.com/paper/2312.16864/) (2023)
- [Intelligent Assistant Language Understanding On Device](https://mlsanity.com/paper/2308.03905/) (2023)
- [Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue](https://mlsanity.com/paper/2406.14457/) (2024)
- [TSCAN : Dialog Structure discovery using SCAN](https://mlsanity.com/paper/2107.06426/) (2021)
- [Revisiting Conversation Discourse for Dialogue Disentanglement](https://mlsanity.com/paper/2306.03975/) (2023)
- [Adapting Text-based Dialogue State Tracker for Spoken Dialogues](https://mlsanity.com/paper/2308.15053/) (2023)
- [Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?](https://mlsanity.com/paper/2212.10504/) (2022)
- [Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System](https://mlsanity.com/paper/2305.16106/) (2023)
- [Learn to Focus: Hierarchical Dynamic Copy Network for Dialogue State Tracking](https://mlsanity.com/paper/2107.11778/) (2021)
- [Dynamic Knowledge Fusion for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2603.10367/) (2026)

## Abstract (arXiv)

Existing approaches to dialogue state tracking rely on pre-defined ontologies consisting of a set of all possible slot types and values. Though such approaches exhibit promising performance on single-domain benchmarks, they suffer from computational complexity that increases proportionally to the number of pre-defined slots that need tracking. This issue becomes more severe when it comes to multi-domain dialogues which include larger numbers of slots. In this paper, we investigate how to approach DST using a generation framework without the pre-defined ontology list. Given each turn of user utterance and system response, we directly generate a sequence of belief states by applying a hierarchical encoder-decoder structure. In this way, the computational complexity of our model will be a constant regardless of the number of pre-defined slots. Experiments on both the multi-domain and the single domain dialogue state tracking dataset show that our model not only scales easily with the increasing number of pre-defined domains and slots but also reaches the state-of-the-art performance.