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

TL;DR

This paper introduces a contrastive pre-training and fine-tuning approach for few-shot intent detection, effectively distinguishing fine-grained, semantically similar intents with state-of-the-art results.

Contribution

It proposes a novel contrastive learning framework combining self-supervised pre-training and supervised fine-tuning for few-shot intent detection.

Findings

Achieves state-of-the-art performance on three datasets.

Effective in 5-shot and 10-shot scenarios.

Handles fine-grained, semantically similar intents.

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.