Translator can be used in web or client applications on any hardware platform and with any operating system to perform language translation and other language-related operations such as language detection, text to speech, or dictionary. This same service is also accessible, at no additional cost, from within familiar Microsoft products such as Bing, Cortana, Microsoft Edge, Office, SharePoint, Skype, and Yammer. It is incorporated across product localization, support, and online communication teams. Translator is used extensively within Microsoft. Translator has been used by Microsoft groups since 2007 and is available as an API for customers since 2011.
Neither act as bilingual dictionaries, translating words based on a list of potential translations, but translate based on the context of the word that is used in a sentence.Both require large amounts of pre-human translated content (up to millions of translated sentences) to train the systems.This translation technology started deploying for users and developers in the latter part of 2016.īoth SMT and NMT translation technologies have two elements in common: The advent of Neural Machine Translation (NMT) caused a radical shift in translation technology, resulting in much higher quality translations. SMT has been used since the mid-2000s by all major translation service providers, including Microsoft. SMT uses advanced statistical analysis to estimate the best possible translations for a word given the context of a few words. Historically, the primary machine learning technique used in the industry was Statistical Machine Translation (SMT). Since the early 2010s, a new artificial intelligence technology, deep neural networks (aka deep learning), has allowed the technology of speech recognition to reach a quality level that allowed the Microsoft Translator team to combine speech recognition with its core text translation technology to launch a new speech translation technology. The service translates a “source” text from one language to a different “target” language.Īlthough the concepts behind machine translation technology and the interfaces to use it are relatively simple, the science and technologies behind it are extremely complex and bring together several leading-edge technologies, in particular, deep learning (artificial intelligence), big data, linguistics, cloud computing, and web APIs.
Machine translation systems are applications or online services that use machine-learning technologies to translate large amounts of text from and to any of their supported languages.
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