![]() In addition, it ‘learned’ as it was used generating constant improvement in quality. When the small team’s methodology was tested against Google’s main statistical machine translation engine, it proved far faster and more effective across many languages. In 2016, Google had an experimental team testing the use of neural learning models and artificial intelligence (AI) to train machine translation engines. Training these machines involved a lot of manual labor, and each added language required starting over with the development for that language. Early developers used statistical databases of languages to “teach” computers to translate text. It was only in the early 2000s that the software, data, and required hardware became capable of doing basic machine translation. Unfortunately, the complexity of the task was far higher than early computer scientists’ estimates, requiring enormous data processing power and storage far beyond the capabilities of early machines. Translation was one of the first applications of computing power, starting in the 1950s. Machine translation is the process of automatically translating content from one language to another without any human input. Make the most of machine translation with Phrase.Quality estimation for machine translation.Increasing translator productivity and cost reduction. ![]() Using the optimal engine for your content.What is the best machine translation software?.Automated integration into translation workflows.What are the advantages of machine translation?.What are some major machine translation providers?.Stick to human translation when branding and culture come into play.Apply light or full post-editing to more sensitive content.Use raw machine translation for low-impact and unambiguous content.Machine translation vs human translation.Which machine translation type should I use?.What types of machine translation are there?.Automated vs machine translation: What’s the difference?.
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