# Toward Scalable Neural Dialogue State Tracking Model

arXiv: https://arxiv.org/abs/1812.00899 · HTML: https://mlsanity.com/paper/1812.00899/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 11 of 11)

- [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)
- [Pretraining the Noisy Channel Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2103.10518/) (2021)
- [Effective Sequence-to-Sequence Dialogue State Tracking](https://mlsanity.com/paper/2108.13990/) (2021)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools](https://mlsanity.com/paper/2202.07156/) (2022)
- [OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue](https://mlsanity.com/paper/2209.04595/) (2022)
- [Multi-Step Dialogue Workflow Action Prediction](https://mlsanity.com/paper/2311.09593/) (2023)
- [Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead](https://mlsanity.com/paper/2110.12412/) (2021)
- [Schema Encoding for Transferable Dialogue State Tracking](https://mlsanity.com/paper/2210.02351/) (2022)
- [Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling](https://mlsanity.com/paper/2501.10316/) (2025)

## Abstract (arXiv)

The latency in the current neural based dialogue state tracking models prohibits them from being used efficiently for deployment in production systems, albeit their highly accurate performance. This paper proposes a new scalable and accurate neural dialogue state tracking model, based on the recently proposed Global-Local Self-Attention encoder (GLAD) model by Zhong et al. which uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. By using only one recurrent networks with global conditioning, compared to (1 + # slots) recurrent networks with global and local conditioning used in the GLAD model, our proposed model reduces the latency in training and inference times by 35% on average, while preserving performance of belief state tracking, by 97.38% on turn request and 88.51% on joint goal and accuracy. Evaluation on Multi-domain dataset (Multi-WoZ) also demonstrates that our model outperforms GLAD on turn inform and joint goal accuracy.