Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance. While previous works train the model contrastively to increase the margin between different multi-intent labels, they are less suited to the nuances of multi-intent NLU. They ignore the rich information between the shared intents, which is beneficial to constructing a better embedding space, especially in low-data scenarios. We introduce a two-stage Prediction-Aware Contrastive Learning (PACL) framework for multi-intent NLU to harness this valuable knowledge. Our approach capitalizes on shared intent information by integrating word-level pre-training and prediction-aware contrastive fine-tuning. We construct a pre-training dataset using a word-level data augmentation strategy. Subsequently, our framework dynamically assigns roles to instances during contrastive fine-tuning while introducing a prediction-aware contrastive loss to maximize the impact of contrastive learning. We present experimental results and empirical analysis conducted on three widely used datasets, demonstrating that our method surpasses the performance of three prominent baselines on both low-data and full-data scenarios.

翻译：多意图自然语言理解（NLU）因单个话语中包含多个意图导致模型混淆，构成严峻挑战。现有研究通过对比学习增大不同多意图标签间的间隔，但未能充分适配多意图NLU的细粒度特性。这些方法忽略了共享意图间的丰富信息——这类信息对于构建更优的嵌入空间至关重要，尤其在低数据场景中。为挖掘这一宝贵知识，我们提出面向多意图NLU的两阶段预测感知对比学习（PACL）框架。该方法通过融合词级预训练与预测感知对比微调，有效利用共享意图信息：首先采用词级数据增强策略构建预训练数据集，而后在对比微调阶段动态分配实例角色，并引入预测感知对比损失函数以最大化对比学习效果。在三个广泛使用的数据集上的实验结果与实证分析表明，我们的方法在低数据与全数据场景下均显著超越了三种主流基线模型的性能。