  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.
