New Intent Discovery with Attracting and Dispersing Prototype
Shun Zhang Jian Yang Jiaqi Bai Chaoran Yan Tongliang Li Zhao Yan Zhoujun Li
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).