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.

翻译：在意图检测任务中，利用意图标签中的有意义语义信息对少样本场景尤为有益。然而，现有少样本意图检测方法要么忽略意图标签（例如将意图视为索引），要么未充分利用此类信息（例如仅使用部分意图标签）。本研究提出一种端到端的“一对全”（One-to-All）系统，该系统可实现输入话语与所有候选标签的对比，从而充分挖掘标签语义信息。在三个少样本意图检测任务上的实验表明，当训练资源极度匮乏时，“一对全”模型在1-shot、3-shot和5-shot设置下均取得最先进性能，表现尤为突出。此外，我们提出一种新颖的预训练策略，该策略利用来自释义的间接监督，使模型在意图检测任务上实现零样本跨域泛化。我们的代码已开源至 https://github.com/jiangshdd/AllLablesTogether。