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            <title>Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)</title>
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    <abstract>Conversation designers continue to face significant obstacles when creating productionquality task-oriented dialogue systems. The complexity and cost involved in schema development and data collection is often a major barrier for such designers, limiting their ability to create natural, user-friendly experiences. We frame the classification of user intent as the generation of a canonical form, a lightweight semantic representation using natural language. We show that canonical forms offer a promising alternative to traditional methods for intent classification. By tuning soft prompts for a frozen large language model, we show that canonical forms generalize very well to new, unseen domains in a zero- or few-shot setting. The method is also sample-efficient, reducing the complexity and effort of developing new task-oriented dialogue domains.</abstract>
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    <identifier type="doi">10.18653/v1/2022.seretod-1.4</identifier>
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%0 Conference Proceedings
%T Prompt Learning for Domain Adaptation in Task-Oriented Dialogue
%A Sreedhar, Makesh Narsimhan
%A Parisien, Christopher
%Y Ou, Zhijian
%Y Feng, Junlan
%Y Li, Juanzi
%S Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, Beijing (Hybrid)
%F sreedhar-parisien-2022-prompt
%X Conversation designers continue to face significant obstacles when creating productionquality task-oriented dialogue systems. The complexity and cost involved in schema development and data collection is often a major barrier for such designers, limiting their ability to create natural, user-friendly experiences. We frame the classification of user intent as the generation of a canonical form, a lightweight semantic representation using natural language. We show that canonical forms offer a promising alternative to traditional methods for intent classification. By tuning soft prompts for a frozen large language model, we show that canonical forms generalize very well to new, unseen domains in a zero- or few-shot setting. The method is also sample-efficient, reducing the complexity and effort of developing new task-oriented dialogue domains.
%R 10.18653/v1/2022.seretod-1.4
%U https://aclanthology.org/2022.seretod-1.4
%U https://doi.org/10.18653/v1/2022.seretod-1.4
%P 24-30

Markdown (Informal)

[Prompt Learning for Domain Adaptation in Task-Oriented Dialogue](https://aclanthology.org/2022.seretod-1.4) (Sreedhar & Parisien, SereTOD 2022)

• Prompt Learning for Domain Adaptation in Task-Oriented Dialogue (Sreedhar & Parisien, SereTOD 2022)

ACL

• Makesh Narsimhan Sreedhar and Christopher Parisien. 2022. Prompt Learning for Domain Adaptation in Task-Oriented Dialogue. In Proceedings of the Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems (SereTOD), pages 24–30, Abu Dhabi, Beijing (Hybrid). Association for Computational Linguistics.