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Business Intelligence Markup Language (BIML) is a domain-specific XML dialect for defining business intelligence (BI) assets. Biml-authored BI assets can currently be used by the BIDS Helper add-on for Microsoft SQL Server Business Intelligence Development Studio (BIDS) and the Varigence Mist (now called BimlStudio) integrated development environment; both tools translate Biml metadata into ...
The following table compares the number of languages which the following machine translation programs can translate between. (Moses and Moses for Mere Mortals allow you to train translation models for any language pair, though collections of translated texts (parallel corpus) need to be provided by the user.
Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. [3]
Google Translate's NMT system uses a large artificial neural network capable of deep learning. [1] [2] [3] By using millions of examples, GNMT improves the quality of translation, [2] using broader context to deduce the most relevant translation.
Microsoft Translator or Bing Translator is a multilingual machine translation cloud service provided by Microsoft.Microsoft Translator is a part of Microsoft Cognitive Services [1] and integrated across multiple consumer, developer, and enterprise products, including Bing, Microsoft Office, SharePoint, Microsoft Edge, Microsoft Lync, Yammer, Skype Translator, Visual Studio, and Microsoft ...
A typical way for lay people to assess machine translation quality is to translate from a source language to a target language and back to the source language with the same engine. Though intuitively this may seem like a good method of evaluation, it has been shown that round-trip translation is a "poor predictor of quality". [1]
The word-based translation is not widely used today; phrase-based systems are more common. Most phrase-based systems are still using GIZA++ to align the corpus [citation needed]. The alignments are used to extract phrases or deduce syntax rules. [11] And matching words in bi-text is still a problem actively discussed in the community.
SYSTRAN's first implementation system was implemented in 1988 by the online service of the French Postal Service called Minitel. [16] Various computer based translation companies were also launched, including Trados (1984), which was the first to develop and market Translation Memory technology (1989), though this is not the same as MT.