Metric Learning and Adaptive Boundary for Out-of-Domain Detection
This addresses the problem of robust OOD detection for conversational agents in open-world environments, where collecting OOD data is challenging, representing a novel method for a known bottleneck.
The paper tackles out-of-domain (OOD) detection for conversational agents without requiring OOD training data, achieving state-of-the-art performance on public datasets and showing significant OOD improvement in low-class scenarios while maintaining in-domain accuracy.
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.