Google announced results of NIST 2005 Machine Translation Evaluation and revealed their techniques of translation which is based on models learned automaticly from parallel data.
Our approach was to use statistical translation models learned from parallel text, that is, sets of documents and their translations. The system learns a model automatically from the parallel data. This approach differs from the rule-based approach used by many existing commercial machine translation companies which is based on large sets of handwritten translation rules.
This is the favor of being the most popular search engine and owning a huge database that they’re using to make research and create more powerful products.
