  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.
