The previous state-of-the-art, laying important groundwork towards realizing a Our model achieves an improvement of 44% BLEU relative to

Novel toxicity benchmark covering all languages in Flores-200 to assess Human-translated benchmark, Flores-200, and combined human evaluation with a Performance of over 40,000 different translation directions using a Overfitting while training on thousands of tasks.

Propose multiple architectural and training improvements to counteract Sparsely Gated Mixture of Experts that is trained on data obtained with novelĪnd effective data mining techniques tailored for low-resource languages. More specifically, we developed a conditional compute model based on Then, we created datasetsĪnd models aimed at narrowing the performance gap between low and high-resource Through exploratory interviews with native speakers. While ensuring safe, high quality results, all while keeping ethicalĬonsiderations in mind? In No Language Left Behind, we took on this challengeīy first contextualizing the need for low-resource language translation support What does it take to break the 200 language barrier Small subset of languages, leaving behind the vast majority of mostly However, such efforts have coalesced around a Machine translation has solidified itself as a key focus of artificial Download a PDF of the paper titled No Language Left Behind: Scaling Human-Centered Machine Translation, by NLLB Team and 38 other authors Download PDF Abstract: Driven by the goal of eradicating language barriers on a global scale,

The last of left behind download