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Introduction to No Language Left Behind (NLLB-200)

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Meta AI recently open-sourced its massive translation model, No Language Left Behind (NLLB-200), intending to exclude language barriers across the globe. As we know, that machine translation has become a key area of research nowadays, and it has become a great news for many researchers and organisations who can use it for their respective research and work. So let’s take a look at the news and understand a bit about NLLB-200 with the below points:

Table of contents

  • What is NLLB-200?
  • Advancement in NLLB-200
  • Where is NLLB-200 applied?
  • The training procedure for NLLB-200

What is NLLB-200?

No Language Left Behind (NLLB-200) is a model from the series of massive machine translation models of MetaAI for language translation. A newer member of the series NLLB-200 is capable of translating between 200 languages, representing Meta’s capacity of Meta in the direction of AI researchers. These development aims to allow people to access, share and use online content in their native languages and communicate across the world regardless of language preferences.

Advancement in NLLB-200

Some of the significant advancements in this model are:

  • According to the Meta AI research team, this model can provide 44% better results than the previous versions.
  • This model is also capable of translating languages Kamba and Lao. These languages were not translated before using any of the machine translation methods available freely.
  • To train this model, they have generated a new dataset named Flores-200 consisting of data in 200 languages.
  • Evaluation of this model has gone through 40,000 different translation directions.
  • It can translate content from one language to another without using any intermediate language, for example, directly from Hindi to Thai.

Where is NLLB-200 applied?

Meta AI is utilising this project on their platform on Facebook and Instagram to enhance communication in various languages. These platforms are highly responsible for connecting people together. According to an article on statista there are roughly 2.93 billion active users of Facebook in the first quarter of 2022. Looking at these statistics, we can understand how useful this model is for not only Facebook but also for the people using Facebook because they become more effective when using their native languages.

The technology behind this project is also available with the Wikimedia Foundation’s Content Translation tool, which helps Wikipedia to translate the content into different languages. This is very beneficial for the world because it will produce more knowledge in more languages.

The training procedure for NLLB-200

These three major steps involved in the training procedure of NLLB-200:

  1. Automatic data construction: To make this model work on low-resource languages, Meta used the teacher-student training procedure where the older LASER(Language-Agnostic SEntence Representation) model is trained on 200 languages and produced a huge amount of data for training of NLLB-200.
  2. Modelling 200 languages: A large number of expert models has been utilised in the modelling so that every category of the data or language can be routed under the shared capacity settings of models. In addition, regularisation techniques are utilised to avoid overfitting of the models. This step can be compared with the normal machine translation modelling procedure where one encoder and one decoder are utilised first to translate word data into numerical form and then again into the word form.
  3. Result Evaluation: For evaluation of the results from NLLB-200, meta has extended their dataset FLORES to cover 200 languages. The dataset consists of human-translated data. Utilising various metrics and human evaluation support, they have validated that the model is producing 44% better results than Meta’s older machine translation models.

Final words

In this article, we have gone through big news in the field of machine translation modelling. NLLB-200 is one of the major changes because it’s being utilised for translation between 200 languages. In advancement, we can see the major directions where this field is extending itself. This technology has helped advance some of the major platforms that are required to deal with considerable language barriers. Training procedures of these models can help in understanding the procedure of modelling big algorithms. We can learn more about this open-source model here.

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