# Domain State Tracking for a Simplified Dialogue System

arXiv: https://arxiv.org/abs/2103.06648 · HTML: https://mlsanity.com/paper/2103.06648/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context](https://mlsanity.com/paper/1911.10484/) (2019)
- [SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching](https://mlsanity.com/paper/2005.05298/) (2020)
- [A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning](https://mlsanity.com/paper/2009.08115/) (2020)
- [Structured Fusion Networks for Dialog](https://mlsanity.com/paper/1907.10016/) (2019)
- [UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues](https://mlsanity.com/paper/2004.14307/) (2020)

## Built on by (most cited first, 17 of 17)

- [GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection](https://mlsanity.com/paper/2111.14592/) (2021)
- [Guiding Large Language Models via Directional Stimulus Prompting](https://mlsanity.com/paper/2302.11520/) (2023)
- [Shades of BLEU, Flavours of Success: The Case of MultiWOZ](https://mlsanity.com/paper/2106.05555/) (2021)
- [SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation](https://mlsanity.com/paper/2209.06664/) (2022)
- [Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System](https://mlsanity.com/paper/2305.02468/) (2023)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog](https://mlsanity.com/paper/2205.02471/) (2022)
- [Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog](https://mlsanity.com/paper/2210.08917/) (2022)
- [KRLS: Improving End-to-End Response Generation in Task Oriented Dialog with Reinforced Keywords Learning](https://mlsanity.com/paper/2211.16773/) (2022)
- [OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System](https://mlsanity.com/paper/2312.16864/) (2023)
- [Mitigating Negative Style Transfer in Hybrid Dialogue System](https://mlsanity.com/paper/2212.07183/) (2022)
- [Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue](https://mlsanity.com/paper/2406.14457/) (2024)
- [Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System](https://mlsanity.com/paper/2305.16106/) (2023)
- [TA&AT: Enhancing Task-Oriented Dialog with Turn-Level Auxiliary Tasks and Action-Tree Based Scheduled Sampling](https://mlsanity.com/paper/2401.15626/) (2024)
- [Many Hands Make Light Work: Task-Oriented Dialogue System with Module-Based Mixture-of-Experts](https://mlsanity.com/paper/2405.09744/) (2024)
- [Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning](https://mlsanity.com/paper/2411.05340/) (2024)
- [Collaborative and Proactive Management of Task-Oriented Conversations](https://mlsanity.com/paper/2510.05110/) (2025)

## Abstract (arXiv)

Task-oriented dialogue systems aim to help users achieve their goals in specific domains. Recent neural dialogue systems use the entire dialogue history for abundant contextual information accumulated over multiple conversational turns. However, the dialogue history becomes increasingly longer as the number of turns increases, thereby increasing memory usage and computational costs. In this paper, we present DoTS (Domain State Tracking for a Simplified Dialogue System), a task-oriented dialogue system that uses a simplified input context instead of the entire dialogue history. However, neglecting the dialogue history can result in a loss of contextual information from previous conversational turns. To address this issue, DoTS tracks the domain state in addition to the belief state and uses it for the input context. Using this simplified input, DoTS improves the inform rate and success rate by 1.09 points and 1.24 points, respectively, compared to the previous state-of-the-art model on MultiWOZ, which is a well-known benchmark.