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Why is customized machine translation better than generic machine translation?

Generic machine translation engines like Google Translate or Bing Translator are designed to translate anything with no specific domain bias or focus. The resulting translation is often out of context and lacks any specific writing style. Terminology and vocabulary choice are often inconsistent. Worse still, the terminology can often be completely wrong as it was determined by what was which data was most statistically relevant.

To demonstrate this concept, consider the simple sentence “I went to the bank.” A generic translation engine will not know if the context is “I went to the back (of the river)” or “I went to the back (of the turn)” or “I went to the back (to deposit money)” or any of a number of other possible contexts. As such, the most statistically relevant choice will be taken, which in many cases will be wrong.

From the single sentence above, even a human translator cannot determine the context, so it is reasonable that a machine would also have difficulty. However, if a human translator were given the information that they were translating in the Banking and Finance domain, then the accuracy of the translation would be increased. Customization of a machine translation engine is similar in that an engine is trained in a specific domain so has additional contextual information that improves translation accuracy.

There are different levels of customization and different approaches to customization. The full machine translation customization approach provided by Language Studio™ incorporates the Clean Data SMT model and is the most advanced in the industry. This approach ensures the context, preferred terminology, vocabulary choice and writing style are incorporated into the machine learning process and thus deliver optimal machine translation quality output.