# Efficient Dialogue State Tracking by Selectively Overwriting Memory

arXiv: https://arxiv.org/abs/1911.03906 · HTML: https://mlsanity.com/paper/1911.03906/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2019.

## Built on by (most cited first, 30 of 76)

- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Dialogue State Tracking with a Language Model using Schema-Driven Prompting](https://mlsanity.com/paper/2109.07506/) (2021)
- [MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation](https://mlsanity.com/paper/2104.00773/) (2021)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [Keep Me Updated! Memory Management in Long-term Conversations](https://mlsanity.com/paper/2210.08750/) (2022)
- [Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking](https://mlsanity.com/paper/2105.04222/) (2021)
- [Zero-Shot Dialogue State Tracking via Cross-Task Transfer](https://mlsanity.com/paper/2109.04655/) (2021)
- [Description-Driven Task-Oriented Dialog Modeling](https://mlsanity.com/paper/2201.08904/) (2022)
- [In-Context Learning for Few-Shot Dialogue State Tracking](https://mlsanity.com/paper/2203.08568/) (2022)
- [ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?](https://mlsanity.com/paper/2306.01386/) (2023)
- [Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking](https://mlsanity.com/paper/2106.00291/) (2021)
- [Zero-shot Generalization in Dialog State Tracking through Generative Question Answering](https://mlsanity.com/paper/2101.08333/) (2021)
- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- ["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)
- [Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking](https://mlsanity.com/paper/2203.01552/) (2022)
- [Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking](https://mlsanity.com/paper/2104.04466/) (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)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking](https://mlsanity.com/paper/2402.02285/) (2024)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Task-Oriented Dialogue System as Natural Language Generation](https://mlsanity.com/paper/2108.13679/) (2021)
- [Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level Performances](https://mlsanity.com/paper/2204.03375/) (2022)
- [ASSIST: Towards Label Noise-Robust Dialogue State Tracking](https://mlsanity.com/paper/2202.13024/) (2022)
- [Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues](https://mlsanity.com/paper/2111.02574/) (2021)
- [UniDU: Towards A Unified Generative Dialogue Understanding Framework](https://mlsanity.com/paper/2204.04637/) (2022)
- [Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries](https://mlsanity.com/paper/2402.13043/) (2024)
- [Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations](https://mlsanity.com/paper/2107.05168/) (2021)

## Abstract (arXiv)

Recent works in dialogue state tracking (DST) focus on an open vocabulary-based setting to resolve scalability and generalization issues of the predefined ontology-based approaches. However, they are inefficient in that they predict the dialogue state at every turn from scratch. Here, we consider dialogue state as an explicit fixed-sized memory and propose a selectively overwriting mechanism for more efficient DST. This mechanism consists of two steps: (1) predicting state operation on each of the memory slots, and (2) overwriting the memory with new values, of which only a few are generated according to the predicted state operations. Our method decomposes DST into two sub-tasks and guides the decoder to focus only on one of the tasks, thus reducing the burden of the decoder. This enhances the effectiveness of training and DST performance. Our SOM-DST (Selectively Overwriting Memory for Dialogue State Tracking) model achieves state-of-the-art joint goal accuracy with 51.72% in MultiWOZ 2.0 and 53.01% in MultiWOZ 2.1 in an open vocabulary-based DST setting. In addition, we analyze the accuracy gaps between the current and the ground truth-given situations and suggest that it is a promising direction to improve state operation prediction to boost the DST performance.