meistervova.blogg.se - Machine translation vs transliteration

Statistical modeling techniques and efficient algorithms help the computer address the problem of decipherment (detecting the correspondences between source and target language in the training data) and decoding (finding the best translation of a new input sentence). So-called “parallel corpora” act as a modern Rosetta Stone in massive proportions, providing word, phrase, and idiomatic translations in context for many language pairs and domains. Rather than writing hand-crafted rules to translate between languages, modern translation systems approach translation as a problem of learning the transformation of text between languages from existing human translations and leveraging recent advances in applied statistics and machine learning. Translator’s implementation of Statistical Machine Translation (SMT) is built on more than a decade of natural-language research at Microsoft. The Translator service is an Azure service hosted in Microsoft data centers and benefits from the security, scalability, reliability, and nonstop availability that other Microsoft cloud services also receive.

Leveraging industry standard REST technology, the developer sends source text (or audio for speech translation) to the service with a parameter indicating the target language, and the service sends back the translated text for the client or web app to use. 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.