Work

Survey of Low-Resource Machine Translation

Year: 2021

Type: preprint

Abstract: We present a survey covering the state of the art in low-resource machine translation research. There are currently around 7000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the challenge of producing useful translation models when very little translated training data is available. We present a summary of this topical research field and provide a description of the techniques evaluated by researchers in several recent shared tasks in low-resource MT.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2109.00486

Authors: Haddow, Barry, Bawden, Rachel, Barone, Antonio Valerio Miceli, Helcl, Jindřich, Birch, Alexandra

Institutions: University of Edinburgh, ALMANACH: Modélisation et analyse linguistique automatique et humanités computationnelles

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Agence Nationale de la Recherche, Engineering and Physical Sciences Research Council

Awards: G3010527862, MTStretch: Low-resource Machine Translation, PaRis Artificial Intelligence Research InstitutE, Global Under-Resourced MEedia Translation

OpenAlex ID: W4225929449

Cites:

Cited by: 1

Related to: 10