Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning

Abstract

In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-shot intent detection schema via contrastive pre-training and fine-tuning. Specifically, we first conduct self-supervised contrastive pre-training on collected intent datasets, which implicitly learns to discriminate semantically similar utterances without using any labels. We then perform few-shot intent detection together with supervised contrastive learning, which explicitly pulls utterances from the same intent closer and pushes utterances across different intents farther. Experimental results show that our proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings.

Accepted by EMNLP 2021 main conference

References in corpus (5)

• Learning Transferable Visual Models From Natural Language Supervision
• CLEAR: Contrastive Learning for Sentence Representation
• DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue
• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
• CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection