Metric Learning and Adaptive Boundary for Out-of-Domain Detection

TL;DR

This paper introduces a novel OOD detection method combining metric learning with adaptive boundaries, which outperforms existing algorithms especially in low-class scenarios without requiring OOD data.

Contribution

The paper proposes a data-independent OOD detection algorithm that enhances robustness by integrating metric learning with adaptive decision boundaries.

Findings

Outperforms state-of-the-art algorithms on public datasets.

Improves OOD detection in low-class scenarios.

Maintains high in-domain classification accuracy.

Abstract

Conversational agents are usually designed for closed-world environments. Unfortunately, users can behave unexpectedly. Based on the open-world environment, we often encounter the situation that the training and test data are sampled from different distributions. Then, data from different distributions are called out-of-domain (OOD). A robust conversational agent needs to react to these OOD utterances adequately. Thus, the importance of robust OOD detection is emphasized. Unfortunately, collecting OOD data is a challenging task. We have designed an OOD detection algorithm independent of OOD data that outperforms a wide range of current state-of-the-art algorithms on publicly available datasets. Our algorithm is based on a simple but efficient approach of combining metric learning with adaptive decision boundary. Furthermore, compared to other algorithms, we have found that our proposed algorithm has significantly improved OOD performance in a scenario with a lower number of classes while preserving the accuracy for in-domain (IND) classes.