Out-of-Domain (OOD) intent classification is an important task for a dialog system, as it allows for appropriate responses to be generated. Previous studies aiming to solve the OOD intent classification task have generally adopted metric learning methods to generate decision boundaries in the embedding space. However, these existing methods struggle to capture the high-dimensional semantic features of data, as they learn decision boundary using scalar distances. They also use generated OOD samples for learning. However, such OOD samples are biased, and they cannot include all real-world OOD intents, thus representing a limitation. In the current paper, we attempt to overcome these challenges by using Advanced Proxy-Anchor loss, which introduces a margin proxy and shared proxy. First, to generate a decision boundary that has the high-dimensional semantic features of training data, we use a margin proxy for learnable embedding vectors. Next, the shared proxy, which is shared by all In-Domain (IND) samples, is introduced to make it possible to learn the discriminative feature between IND intents and OOD intent, ultimately leading to the improved classification of OOD samples. We conduct evaluations of the proposed method using three benchmark datasets. The experimental results demonstrate that our method achieved an improved performance compared to the methods described in previous studies.
