A Gentle Introduction to Neural Machine Translation

Last Updated on August 7, 2019

One of the earliest goals for computers was the automatic translation of text from one language to another.

Automatic or machine translation is perhaps one of the most challenging artificial intelligence tasks given the fluidity of human language. Classically, rule-based systems were used for this task, which were replaced in the 1990s with statistical methods. More recently, deep neural network models achieve state-of-the-art results in a field that is aptly named neural machine translation.

In this post, you will discover the challenge of machine translation and the effectiveness of neural machine translation models.

After reading this post, you will know:

• Machine translation is challenging given the inherent ambiguity and flexibility of human language.
• Statistical machine translation replaces classical rule-based systems with models that learn to translate from examples.
• Neural machine translation models fit a single model rather than a pipeline of fine-tuned models and currently achieve state-of-the-art results.

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