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
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.findings-emnlp.400.pdf