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 ...

Verfasser: SONG, Yewei
EZZINI, Saad
KLEIN, Jacques
BISSYANDE, Tegawendé François d Assise
Lefebvre, Clément
Goujon, Anne
Dokumenttyp: conference paper
Erscheinungsdatum: 2023
Verlag/Hrsg.: Institute of Electrical and Electronics Engineers Inc.
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
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Link(s) : https://orbilu.uni.lu/handle/10993/57836