Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2022

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 largescale 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 fewshot data. The experiments on a target dataset across multiple domains show that our proposed model brings remarkable performance increases through data augmentation.

Keywords

Computational linguistics, Augmentation methods, Data augmentation; Pre-training

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing EMNLP: Abu Dhabi, December 7-11

First Page

3788

Last Page

3799

Publisher

ACL

City or Country

Stroudsburg

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://aclanthology.org/2022.findings-emnlp.277/

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