# Improving Limited Labeled Dialogue State Tracking with Self-Supervision

arXiv: https://arxiv.org/abs/2010.13920 · HTML: https://mlsanity.com/paper/2010.13920/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [In-Context Learning for Few-Shot Dialogue State Tracking](https://mlsanity.com/paper/2203.08568/) (2022)
- [Large Language Models as Zero-shot Dialogue State Tracker through Function Calling](https://mlsanity.com/paper/2402.10466/) (2024)
- [Controllable Abstractive Dialogue Summarization with Sketch Supervision](https://mlsanity.com/paper/2105.14064/) (2021)
- [Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking](https://mlsanity.com/paper/2203.01552/) (2022)
- [Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning](https://mlsanity.com/paper/2311.03748/) (2023)
- [OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking](https://mlsanity.com/paper/2311.09758/) (2023)
- [Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking](https://mlsanity.com/paper/2307.01453/) (2023)
- [DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning](https://mlsanity.com/paper/2212.02851/) (2022)
- [A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking](https://mlsanity.com/paper/2201.05780/) (2022)
- [Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries](https://mlsanity.com/paper/2402.13043/) (2024)
- [Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic](https://mlsanity.com/paper/2403.17853/) (2024)
- [Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision](https://mlsanity.com/paper/2107.08582/) (2021)
- [CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking](https://mlsanity.com/paper/2210.05146/) (2022)
- [UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking](https://mlsanity.com/paper/2310.10492/) (2023)
- [CorrectionLM: Self-Corrections with SLM for Dialogue State Tracking](https://mlsanity.com/paper/2410.18209/) (2024)
- [State Value Generation with Prompt Learning and Self-Training for Low-Resource Dialogue State Tracking](https://mlsanity.com/paper/2401.16862/) (2024)

## Abstract (arXiv)

Existing dialogue state tracking (DST) models require plenty of labeled data. However, collecting high-quality labels is costly, especially when the number of domains increases. In this paper, we address a practical DST problem that is rarely discussed, i.e., learning efficiently with limited labeled data. We present and investigate two self-supervised objectives: preserving latent consistency and modeling conversational behavior. We encourage a DST model to have consistent latent distributions given a perturbed input, making it more robust to an unseen scenario. We also add an auxiliary utterance generation task, modeling a potential correlation between conversational behavior and dialogue states. The experimental results show that our proposed self-supervised signals can improve joint goal accuracy by 8.95% when only 1% labeled data is used on the MultiWOZ dataset. We can achieve an additional 1.76% improvement if some unlabeled data is jointly trained as semi-supervised learning. We analyze and visualize how our proposed self-supervised signals help the DST task and hope to stimulate future data-efficient DST research.