Facebook Introduces Language AI Model M2M-100 Which Bypasses Reliance on English
The social media giant Facebook has announced a novel MMT model capable of translating 100*100 languages without the reliance on English-centric data. The new model M2M-100 is trained on a total of 2200 language directions. The single multilingual model has achieved a 10 BLEU point improvement when contrasted to English-centric multilingual models. It will improve the quality of translations for billions of people on a daily basis.
Facebook first used new mining strategies to create translation data with the first truly many-to-many data set with 7.5 billion for 100 languages. Afterward, several scaling techniques were used to bring the number to 15 billion parameters wherein the model was able to capture information from related languages and reflected a more diverse script of languages and morphology.
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The biggest challenge of building an MMT model is forming a large volume of quality parallel sentences for translation directions not involving English. Furthermore, the volume of data required for translation grows quadratically with the addition of languages.
Facebook took on the challenge and created 7.5 billion sentences across 100 hundred languages. The firm combined complementary data mining resources including ccAligned, ccMatrix, and LASER. With this M2M-100 model, it created a new LASER 2.0 with enhanced fastText language identification which further enhanced the quality of mining and includes open-sourced training and evaluation scripts.
To compete with this intense, high computational data, Facebook prioritized the most translation requests. It also prioritized mining directions with the highest quality data and the largest quantity of data.
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The organization also came up with a new bridge mining strategy that can group languages based on classification, geography, and cultural similarities.
This combination of the new bridge and back-translated data improved performance when contrasted with mined data alone. Facebook also found enhanced results on zero-shot settings, where there are no training data available for a pair of languages. The company said that it was substantially better than English-centric models.
The new model is also the first to use Fairscale, the new PyTorch library designed to support pipeline and tensor parallelism.
Such language models will help researchers to put their best foot forward towards creating a single universal language model that can be deployed across different tasks. It will further advance the industry in the creation of a single model supporting all languages, keep translations up-to-date and ultimately, benefit the people.
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