Intent Detection is one of the core tasks of dialog systems. Few-shot Intent Detection is challenging due to limited number of annotated utterances for novel classes. Generalized Few-shot intent detection is more realistic but challenging setup which aims to discriminate the joint label space of both novel intents which have few examples each and existing intents consisting of enough labeled data. Large label spaces and fewer number of shots increase the complexity of the task. In this work, we employ a simple and effective method based on Natural Language Inference that leverages the semantics in the class-label names to learn and predict the novel classes. Our method achieves state-of-the-art results on 1-shot and 5-shot intent detection task with gains ranging from 2-8\% points in F1 score on four benchmark datasets. Our method also outperforms existing approaches on a more practical setting of generalized few-shot intent detection with gains up to 20% F1 score. We show that the suggested approach performs well across single and multi domain datasets with the number of class labels from as few as 7 to as high as 150.

翻译：意图检测是对话系统的核心任务之一。由于新类别标注话语数量有限，小样本意图检测具有挑战性。泛化小样本意图检测是一种更现实但更具挑战性的设置，旨在区分由少量样本组成的新颖意图和拥有充足标注数据的现有意图构成的联合标签空间。大标签空间和少量样本增加了任务复杂度。本研究采用一种基于自然语言推理的简单有效方法，利用类别标签名称中的语义信息学习并预测新类别。该方法在1-shot和5-shot意图检测任务中达到了当前最优结果，在四个基准数据集上的F1分数提升范围为2-8%。在更实际的泛化小样本意图检测设置中，该方法同样优于现有方法，F1分数提升高达20%。我们证明，所提方法在单领域和多领域数据集上均表现良好，标签数量从最少7个到最多150个不等。