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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]
Name Supported File Formats OS Language Widget tool License Across Language Server [2]: Microsoft Word (DOC, DOT, DOCX, and DOCM files), Microsoft Excel (XLS files, and XLSX and XLSM files), Microsoft PowerPoint (PPT and PPS files as well as PPTX, PPSX, and PPTM files), Rich Text Format1 (RTF files), text files (TXT files), TeX (TEX files), HTML, XHTML, XML, SGML, Adobe FrameMaker (in MIF ...
Users enter text into the source field which is translated by a human translator through the Stepes mobile app. Requesting users can see their translation progress live. For most requests, the translation is completed within minutes and appears in the target field for the requesting user to see.
DeepL for Windows translating from Polish to French. The translator can be used for free with a limit of 1,500 characters per translation. Microsoft Word and PowerPoint files in Office Open XML file formats (.docx and .pptx) and PDF files up to 5MB in size can also be translated.
Gettext also provides msgmerge that makes merging translations easy. Vim (Linux/Unix and Windows versions available) with PO ftplugin for easier editing of GNU gettext PO files. gtranslator for Linux; Virtaal: Linux and Windows; for Mac OS X 10.5 and newer a Beta release Native support for Gettext PO translation as well as XLIFF and other ...
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Machine translation is use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Early approaches were mostly rule-based or statistical. These methods have since been superseded by neural machine translation [1] and large language models ...
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