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- W4285246543 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." @default.
- W4285246543 created "2022-07-14" @default.
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- W4285246543 date "2022-01-01" @default.
- W4285246543 modified "2023-10-01" @default.
- W4285246543 title "Metric Learning and Adaptive Boundary for Out-of-Domain Detection" @default.
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- W4285246543 doi "https://doi.org/10.1007/978-3-031-08473-7_12" @default.
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