A Unified Dialogue User Simulator for Few-shot Data Augmentation

Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang

Abstract
Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment large-scale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with few-shot data. The experiments on a target dataset across multiple domains show that our proposed model brings remarkable performance increases through data augmentation.
Anthology ID:
2022.findings-emnlp.277
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3788–3799
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.277
DOI:
10.18653/v1/2022.findings-emnlp.277
Bibkey:
Cite (ACL):
Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, and Minlie Huang. 2022. A Unified Dialogue User Simulator for Few-shot Data Augmentation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3788–3799, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
A Unified Dialogue User Simulator for Few-shot Data Augmentation (Wan et al., Findings 2022)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2022.findings-emnlp.277.pdf
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