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Intent detection is one of the core components of goal-oriented dialogsystems, and detecting out-of-scope (OOS) intents is also a practicallyimportant skill. Few-shot learning is attracting much attention to mitigatedata scarcity, but OOS detection becomes even more challenging. In this paper,we present a simple yet effective approach, discriminative nearest neighborclassification with deep self-attention. Unlike softmax classifiers, weleverage BERT-style pairwise encoding to train a binary classifier thatestimates the best matched training example for a user input. We propose toboost the discriminative ability by transferring a natural language inference(NLI) model. Our extensive experiments on a large-scale multi-domain intentdetection task show that our method achieves more stable and accurate in-domainand OOS detection accuracy than RoBERTa-based classifiers and embedding-basednearest neighbor approaches. More notably, the NLI transfer enables our 10-shotmodel to perform competitively with 50-shot or even full-shot classifiers,while we can keep the inference time constant by leveraging a faster embeddingretrieval model.

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference