1️⃣0️⃣0️⃣0️⃣ Edge#230: How Amazon Scaled Alexa to 1000 Languages

Self-Supervised pretraining, transfer learning and knowledge distillation were among the techniques used to scale Alexa across many languages

On Thursdays, we dive deep into one of the freshest research papers or technology frameworks that is worth your attention. Our goal is to keep you up to date with new developments in AI to complement the concepts we debate in other editions of our newsletter.

💥 What’s New in AI: How Amazon Scaled Alexa to 1000 Languages

In recent years, we have seen an explosion of multilanguage models across different natural language understanding (NLU) tasks. Digital assistants have been one of the most fertile environments to test multilanguage models at scale. One of the many challenges that digital assistants have surfaced is the difference between mastering tasks in high-resource languages like English, French, and Spanish and low-resource languages that are not spoken by large populations. Building a comprehensive experience across both high and resource languages is far from being an easy endeavor. Recently, Amazon Research disclosed some of the techniques they have been implementing in order to scale Alexa to 1000 languages.