Three-way open intent classification with nearest centroid-based representation

Abstract

Open intent classification aims to identify the unknown (open) intents and simultaneously classify the known ones under the open-world assumption. However, the existing studies still face two challenges, i.e., coarse-grained representation learning and uncertain decision boundary. On the one hand, previous methods viewed each class as a unified entity during representation learning, which fails to capture the fine-grained intra-class data structure. On the other hand, traditional two-way decision for open classification struggle to classify the uncertain samples distributed at the edge of the decision boundary, increasing the risk of misclassification. To overcome these limitations, we present a three-way open intent classification method that utilizes the nearest centroid to learn representations, named 3WNC-Open. Specifically, we learn a structured representation by extracting fine-grained information from the sub-classes within each class. Then, we design a three-way open classification strategy to handle uncertainty, initially identifying uncertain samples and then processing them using an effective alternative approach. Experiments on challenging datasets demonstrate that 3WNC-Open outperforms strong baselines.