One Model for Many Languages — Translation Made Simpler

Imagine one system that can translate many tongues at once, faster to build and easier to grow. Researchers start with a pretrained model that already learned from lots of text, then they teach it many directions of translation together. This approach helps when there is little paired data, so low-resource languages get better help than before. The trick is to fine-tune one big model for many language pairs at the same time, rather than training separate ones, and it keeps getting better as new languages are added without hurting old skills. They also created a shared test set called ML50 to compare results fairly, so everyone can check how well models do across easy and hard languages. In many cases this method gives noticeably better translation quality than older ways, with simpler training and more reuse of what the model already learned. It means less work to add languages, and more people can read and share content in their own tongue — a small change that can have a big impact.

Read article comprehensive review in Paperium.net: Multilingual Translation with Extensible Multilingual Pretraining and Finetuning

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