Letz Translate: Low-Resource Machine Translation for Luxembourgish
peer reviewed ; Natural language processing of Low-Resource Languages (LRL) is often challenged by the lack of data. Therefore, achieving accurate machine translation (MT) in a low-resource environment is a real problem that requires practical solutions. Research in multilingual models have shown that some LRLs can be handled with such models. However, their large size and computational needs make their use in constrained environments (e.g., mobile/IoT devices or limited/old servers) impractical. In this paper, we address this problem by leveraging the power of large multilingual MT models usi... Mehr ...
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Dokumenttyp: | conference paper |
Erscheinungsdatum: | 2023 |
Verlag/Hrsg.: |
Institute of Electrical and Electronics Engineers Inc.
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Schlagwörter: | Knowledge distillation / Low-resource Languages / Low-resource Translation / Luxembourgish / Neural Machine Translation / Language processing / Low resource languages / Machine translations / Natural languages / Practical solutions / Real problems / Resources environments / Artificial Intelligence / Computer Science Applications / Computer Vision and Pattern Recognition / Signal Processing / Engineering / computing & technology / Computer science / Ingénierie / informatique & technologie / Sciences informatiques |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-27134239 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://orbilu.uni.lu/handle/10993/57836 |