  Conversation designers continue to face significant obstacles when creating
production quality 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.
