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Domain-specific MT for Low-resource Languages: The case of Bambara-French

Domaine:

natural language processing

Type de record:

datasetpaper
Créateur:
TapLeventhal, MichaelLugHom
Éditeur:
arXiv
Hôte:avatar
Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data. In this paper we address the issue of domain-specific MT for Bambara, an under-resourced Mande language spoken in Mali. We present the first domain-specific parallel dataset for MT of Bambara into and from French. We discuss challenges in working with small quantities of domain-specific data for a low-resource language and we present the results of machine learning experiments on this data.

Visit

doi.orgarxiv.org

Tasks

machine translation

Languages

BamanankanLameMandinkaManinkakan, EasternNomaandeTobanga

Tags

Computation and Language (cs.CL)FOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

arXiv.org perpetual, non-exclusive licensehttp://arxiv.org/licenses/nonexclusive-distrib/1.0/

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