Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary
This work addresses open intent classification for dialogue systems, representing an incremental improvement over existing boundary-based methods.
The paper tackles the problem of open intent classification in dialogue systems by addressing the limitations of prior boundary-based methods that assume compact spherical regions, proposing a multi-granularity method with adaptive granular-ball decision boundaries to better distinguish known intents from unknowns, achieving effectiveness demonstrated through experiments on three public datasets.
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.