Few-shot Natural Language Generation for Task-Oriented Dialog

Baolin Peng, Chenguang Zhu, Chunyuan Li, Xiujun Li, Jinchao Li, Michael Zeng, Jianfeng Gao

Abstract
As a crucial component in task-oriented dialog systems, the Natural Language Generation (NLG) module converts a dialog act represented in a semantic form into a response in natural language. The success of traditional template-based or statistical models typically relies on heavily annotated data, which is infeasible for new domains. Therefore, it is pivotal for an NLG system to generalize well with limited labelled data in real applications. To this end, we present FewshotWOZ, the first NLG benchmark to simulate the few-shot learning setting in task-oriented dialog systems. Further, we develop the SC-GPT model. It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains. Experiments on FewshotWOZ and the large Multi-Domain-WOZ datasets show that the proposed SC-GPT significantly outperforms existing methods, measured by various automatic metrics and human evaluations.
Anthology ID:
2020.findings-emnlp.17
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:
172–182
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.17
DOI:
10.18653/v1/2020.findings-emnlp.17
Bibkey:
Cite (ACL):
Baolin Peng, Chenguang Zhu, Chunyuan Li, Xiujun Li, Jinchao Li, Michael Zeng, and Jianfeng Gao. 2020. Few-shot Natural Language Generation for Task-Oriented Dialog. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 172–182, Online. Association for Computational Linguistics.
Cite (Informal):
Few-shot Natural Language Generation for Task-Oriented Dialog (Peng et al., Findings 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/2020.findings-emnlp.17.pdf
Code
 pengbaolin/SC-GPT +  additional community code
Data
E2E, MultiWOZ
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