Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection

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.