Interval evaluation of temporal (in)stability for neural machine translation
Abstract Though neural machine translation (NMT) has become the leading machine translation (MT) paradigm, its output may still contain errors. To improve NMT quality, it is important to investigate these errors and to see how NMT quality changes with time. The primary focus of the paper is on what...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
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| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00222-y |
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