| Title: |
Metric Learning and Adaptive Boundary for Out-of-Domain Detection |
| Authors: |
Lorenc, Petr, Gargiani, Tommaso, Pichl, Jan, Konrád, Jakub, Marek, Petr, Kobza, Ondřej, Šedivý, Jan |
| Publication Year: |
2022 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computation and Language |
| More Details: |
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.
Comment: Accepted to The 27th International Conference on Natural Language & Information Systems (NLDB) 2022 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2204.10849 |
| Accession Number: |
edsarx.2204.10849 |
| Database: |
arXiv |
