Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary

• URL: http://arxiv.org/abs/2412.13542v1
• Date: Wed, 18 Dec 2024 06:42:19 GMT
• Title: Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary
• Authors: Yanhua Li, Xiaocao Ouyang, Chaofan Pan, Jie Zhang, Sen Zhao, Shuyin Xia, Xin Yang, Guoyin Wang, Tianrui Li,
• Abstract summary: 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. This involves iteratively alternating between adaptive granular-ball clustering and nearest sub-centroid classification to capture fine-grained semantic structures within known intent classes.
• Score: 22.669965275069313
• License:
• Abstract: 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.

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