A unified dialogue user simulator for few-shot data augmentation
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 cor...
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sg-smu-ink.sis_research-85812022-12-12T08:08:46Z A unified dialogue user simulator for few-shot data augmentation WAN, Dazhen ZHANG, Zheng ZHU, Qi LIAO, Lizi HUANG, Minlie 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. 2022-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7578 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=8581&context=sis_research http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Databases and Information Systems |
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Artificial Intelligence and Robotics Databases and Information Systems WAN, Dazhen ZHANG, Zheng ZHU, Qi LIAO, Lizi HUANG, Minlie A unified dialogue user simulator for few-shot data augmentation |
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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. |
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text |
author |
WAN, Dazhen ZHANG, Zheng ZHU, Qi LIAO, Lizi HUANG, Minlie |
author_facet |
WAN, Dazhen ZHANG, Zheng ZHU, Qi LIAO, Lizi HUANG, Minlie |
author_sort |
WAN, Dazhen |
title |
A unified dialogue user simulator for few-shot data augmentation |
title_short |
A unified dialogue user simulator for few-shot data augmentation |
title_full |
A unified dialogue user simulator for few-shot data augmentation |
title_fullStr |
A unified dialogue user simulator for few-shot data augmentation |
title_full_unstemmed |
A unified dialogue user simulator for few-shot data augmentation |
title_sort |
unified dialogue user simulator for few-shot data augmentation |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2022 |
url |
https://ink.library.smu.edu.sg/sis_research/7578 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=8581&context=sis_research |
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