Many-to-English Machine Translation Tools, Data, and Pretrained Models

Domain:

natural language processing

Record type:

papermodelsoftwaredataset

Creator:

GowZhaMatMay

Host:

While there are more than 7000 languages in the world, most translation research efforts have targeted a few high-resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec, and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages. To-appear: ACL 2021 System Demonstrations

Visit

arxiv.org

Tasks

machine translation

Dynamic Aggregation and Augmentation for Low-Resource Machine Translation using Federated Fine-tuning of Pretrained Transformer Models

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# Fine-tuning Multilingual NMT for a Low-Resource, Morphologically Rich Language (Luganda → English)

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Klaimohelmi/yoruba-to-english-neural-machine-translation

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