Improving Limited Labeled Dialogue State Tracking with Self-Supervision

Chien-Sheng Wu, Steven C.H. Hoi, Caiming Xiong


Abstract
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.
Anthology ID:
2020.findings-emnlp.400
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4462–4472
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.400
DOI:
10.18653/v1/2020.findings-emnlp.400
Bibkey:
Cite (ACL):
Chien-Sheng Wu, Steven C.H. Hoi, and Caiming Xiong. 2020. Improving Limited Labeled Dialogue State Tracking with Self-Supervision. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4462–4472, Online. Association for Computational Linguistics.
Cite (Informal):
Improving Limited Labeled Dialogue State Tracking with Self-Supervision (Wu et al., Findings 2020)
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PDF:
https://aclanthology.org/2020.findings-emnlp.400.pdf
Optional supplementary material:
 2020.findings-emnlp.400.OptionalSupplementaryMaterial.pdf