In intent detection tasks, leveraging meaningful semantic information from
intent labels can be particularly beneficial for few-shot scenarios. However,
existing few-shot intent detection methods either ignore the intent labels,
(e.g. treating intents as indices) or do not fully utilize this information
(e.g. only using part of the intent labels). In this work, we present an
end-to-end One-to-All system that enables the comparison of an input utterance
with all label candidates. The system can then fully utilize label semantics in
this way. Experiments on three few-shot intent detection tasks demonstrate that
One-to-All is especially effective when the training resource is extremely
scarce, achieving state-of-the-art performance in 1-, 3- and 5-shot settings.
Moreover, we present a novel pretraining strategy for our model that utilizes
indirect supervision from paraphrasing, enabling zero-shot cross-domain
generalization on intent detection tasks. Our code is at
https://github.com/jiangshdd/AllLablesTogether.Comment: Accepted by IJCNLP-AACL 202