Intent classification (IC) plays an important role in task-oriented dialogue systems as it identifies user intents from given utterances. However, models trained on limited annotations for IC often suffer from a lack of generalization to unseen intent classes. We propose a novel pre-training method for text encoders that uses contrastive learning with intent psuedo-labels to produce embeddings that are well-suited for IC tasks. By applying this pre-training strategy, we also introduce the pre-trained intent-aware encoder (PIE). Specifically, we first train a tagger to identify key phrases within utterances that are crucial for interpreting intents. We then use these extracted phrases to create examples for pre-training a text encoder in a contrastive manner. As a result, our PIE model achieves up to 5.4% and 4.0% higher accuracy than the previous state-of-the-art pre-trained sentence encoder for the N-way zero- and one-shot settings on four IC datasets.

本研究提出一种使用对比学习和伪标签的方法来预训练文本编码器，旨在提高任务导向对话系统中意图分类的性能。通过在四个意图分类数据集上进行实验，我们的预训练意图感知编码器 (PIE) 模型相比于现有最先进的预训练句子编码器，在N路零/一次测试中获得了高达5.4%和4.0%的精度提高。

针对零样本和少样本意图分类的预训练意图感知编码器