Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unknown intents. Prior boundary-based methods assumed known intents fit within compact spherical regions, focusing on coarse-grained representation and precise spherical decision boundaries. However, these assumptions are often violated in practical scenarios, making it difficult to distinguish known intent classes from unknowns using a single spherical boundary. To tackle these issues, we propose a Multi-granularity Open intent classification method via adaptive Granular-Ball decision boundary (MOGB). Our MOGB method consists of two modules: representation learning and decision boundary acquiring. To effectively represent the intent distribution, we design a hierarchical representation learning method. This involves iteratively alternating between adaptive granular-ball clustering and nearest sub-centroid classification to capture fine-grained semantic structures within known intent classes. Furthermore, multi-granularity decision boundaries are constructed for open intent classification by employing granular-balls with varying centroids and radii. Extensive experiments conducted on three public datasets demonstrate the effectiveness of our proposed method.

翻译：开放意图分类对于对话系统的发展至关重要，其目标在于准确将已知意图分类至对应类别，同时识别未知意图。先前基于边界的方法假设已知意图分布于紧凑的球形区域内，侧重于粗粒度表示与精确的球形决策边界。然而，这些假设在实际场景中常被违背，使得使用单一球形边界难以区分已知意图类别与未知意图。为解决这些问题，我们提出一种基于自适应粒球决策边界的多粒度开放意图分类方法（MOGB）。我们的MOGB方法包含两个模块：表示学习与决策边界获取。为有效表征意图分布，我们设计了一种分层表示学习方法。该方法通过自适应粒球聚类与最近邻子质心分类的迭代交替，捕捉已知意图类别内部的细粒度语义结构。此外，通过采用具有不同质心与半径的粒球，构建了适用于开放意图分类的多粒度决策边界。在三个公开数据集上进行的大量实验验证了所提方法的有效性。