  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.
