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        <title>Few-shot Natural Language Generation for Task-Oriented Dialog</title>
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        <namePart type="given">Baolin</namePart>
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        <namePart type="given">Chenguang</namePart>
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        <namePart type="given">Xiujun</namePart>
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    <name type="personal">
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.17</identifier>
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%0 Conference Proceedings
%T Few-shot Natural Language Generation for Task-Oriented Dialog
%A Peng, Baolin
%A Zhu, Chenguang
%A Li, Chunyuan
%A Li, Xiujun
%A Li, Jinchao
%A Zeng, Michael
%A Gao, Jianfeng
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F peng-etal-2020-shot
%X 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.
%R 10.18653/v1/2020.findings-emnlp.17
%U https://aclanthology.org/2020.findings-emnlp.17
%U https://doi.org/10.18653/v1/2020.findings-emnlp.17
%P 172-182

Markdown (Informal)

[Few-shot Natural Language Generation for Task-Oriented Dialog](https://aclanthology.org/2020.findings-emnlp.17) (Peng et al., Findings 2020)

• Few-shot Natural Language Generation for Task-Oriented Dialog (Peng et al., Findings 2020)

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.