# Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification
## 1 Introduction  
Recently, research examining conversation systems has been actively conducted, which has made the accurate classification of user utterance intent increasingly important [1, 2, 3, 4]. In particular, a task-oriented dialog system classifies the utterances that are input by the user into pre-defined intents; this is an essential function for a conversation process [5, 6, 7]. For example, as shown in Figure 1, a banking system classifies each user utterances as a banking intention such as a deposit, withdrawal, and balance check, and then proceeds with the follow-up task, accordingly. However, even if a user inputs an utterance with an intention that is difficult to classify as one of the pre-defined intents (e.g., asking a banking chatbot to make a hotel reservation) is entered, it is necessarily classified as one of the pre-defined banking words. As mentioned above, intents that are not pre-defined in the dialog system are referred to as an OOD intent. It is essential to use an OOD intent classification technique for a dialog system to prepare for the situation when a user inputs OOD intent utterances [8]. To solve this problem, many researchers regard the OOD intent classification task as a (k + 1)-class classification task. For example, [PERSON] et al. [9] propose the concept of open space risk as an evaluation metric for OOD classification. Intuitively, open space risk means that data that have not been experienced during the training process can be located anywhere in the embedding space. [PERSON] et al. [10] propose an Adaptive Decision Boundary (ADB) method that optimizes the decision boundary. To reduce the open spacerisk, that method limits the space in which OOD samples can exist inside the decision boundary by making the intra-class variance small and the inter-classes variance large. However, since the OOD intent can be located anywhere in the embedding space, this method is vulnerable in cases where OOD samples are embedded inside the In-Domain (IND) decision boundary. Moreover, the decision boundaries generated through ADB face difficulty when attempting to learning the high-dimensional semantic feature of data embeddings. To solve this problem, [PERSON] et al. [11] propose the KNN-Contrastive learning method. This method is designed to avoid the risk of OOD samples being located inside the IND decision boundary. However, it experiences instability when sampling random k neighbors. [PERSON] et al. [12] generate OOD samples by utilizing a self-supervised learning method and propose an Adaptive Soft pseudo labeling (ASoul) method that labels each data with a soft labeling technique rather than one hot encoding. However, the OOD samples generated in this way are biased, and there is a limitation in that they cannot include all real-world OOD intents [11; 13; 14]. In this paper, we propose Advanced Proxy-Anchor loss, which captures the high-dimensional semantic features of data and learns the discriminative features between OOD intents and IND intents without needing to use OOD intent samples in training. Our method performs an OOD intent classification task by introducing a margin proxy and shared proxy to the Proxy-Anchor loss [15], which is an existing distance-based classification approach. The proxy, which is a concept that was introduced by [PERSON] et al. [16] in the metric learning approach, is an embedding vector that learns the distances from samples and represents each class of the training dataset. In previous studies [10; 17], the decision boundary has been created by directly calculating the scalar distance from the center of the IND intents when learning the decision boundary. However, since these methods only learn whether each sample is located inside the decision boundary, it is difficult for them to capture high-dimensional features. To overcome this limitation, we utilize the margin proxy in the proposed model.