# Modeling Long Context for Task-Oriented Dialogue State Generation

arXiv: https://arxiv.org/abs/2004.14080 · HTML: https://mlsanity.com/paper/2004.14080/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues](https://mlsanity.com/paper/2111.02574/) (2021)
- [FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis](https://mlsanity.com/paper/2210.15360/) (2022)
- [LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking](https://mlsanity.com/paper/2205.02550/) (2022)
- [Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking](https://mlsanity.com/paper/2302.05932/) (2023)
- [Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking](https://mlsanity.com/paper/2107.12578/) (2021)
- [SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task](https://mlsanity.com/paper/2209.07742/) (2022)
- [A Multi-view Discourse Framework for Integrating Semantic and Syntactic Features in Dialog Agents](https://mlsanity.com/paper/2504.09073/) (2025)

## Abstract (arXiv)

Based on the recently proposed transferable dialogue state generator (TRADE) that predicts dialogue states from utterance-concatenated dialogue context, we propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. By enabling the model to learn a better representation of the long dialogue context, our approaches attempt to solve the problem that the performance of the baseline significantly drops when the input dialogue context sequence is long. In our experiments, our proposed model achieves a 7.03% relative improvement over the baseline, establishing a new state-of-the-art joint goal accuracy of 52.04% on the MultiWOZ 2.0 dataset.