Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary
| Autor: | Li, Yanhua, Ouyang, Xiaocao, Pan, Chaofan, Zhang, Jie, Zhao, Sen, Xia, Shuyin, Yang, Xin, Wang, Guoyin, Li, Tianrui |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | AAAI2025 |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: This paper has been Accepted on AAAI2025 |
| Databáze: | arXiv |
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