Datasets - How Neural Machine Translation Works
I’m currently working on training client-side translation models for Firefox. These models are small distilled models that can be reasonably downloaded by our users, and run directly on their machines using the CPU. In this article I’m going to explain from a high level how machine translations works.
In order for a translation model to begin learning, it first needs data–a lot of data. This data comes in the form of “sentence pairs”. A “sentence” in this pair could be a paragraph, or a sentence fragment. It is a finite chunk of text that has both sides of a translation. Various datasets use different strategies to gather these sentences. Some are professionally translated. Other types of datasets are “mined” from the internet in order to find what is known as “parallel data”, or documents that are the same, but are in different languages.
English: He did it all as evidence that My Grace was in his heart.” Spanish: Él hizo todo eso como evidencia de que mi gracia estaba en su corazón”.
