New Intent Discovery (NID) aims to recognize known and infer new intent
categories with the help of limited labeled and large-scale unlabeled data. The
task is addressed as a feature-clustering problem and recent studies augment
instance representation. However, existing methods fail to capture
cluster-friendly representations, since they show less capability to
effectively control and coordinate within-cluster and between-cluster
distances. Tailored to the NID problem, we propose a Robust and Adaptive
Prototypical learning (RAP) framework for globally distinct decision boundaries
for both known and new intent categories. Specifically, a robust prototypical
attracting learning (RPAL) method is designed to compel instances to gravitate
toward their corresponding prototype, achieving greater within-cluster
compactness. To attain larger between-cluster separation, another adaptive
prototypical dispersing learning (APDL) method is devised to maximize the
between-cluster distance from the prototype-to-prototype perspective.
Experimental results evaluated on three challenging benchmarks (CLINC, BANKING,
and StackOverflow) of our method with better cluster-friendly representation
demonstrate that RAP brings in substantial improvements over the current
state-of-the-art methods (even large language model) by a large margin (average
+5.5% improvement).

新意图发现（NID）通过有限标记和大规模未标记数据的帮助，旨在识别已知意图并推断出新的意图类别。针对这个任务，我们提出了一个旨在实现全球不同决策边界的鲁棒自适应典型学习（RAP）框架，以解决现有方法在捕捉群集友好的表示方面的问题。