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Sphinx converts reStructuredText files into HTML websites and other formats including PDF, EPub, Texinfo and man. reStructuredText is extensible, and Sphinx exploits its extensible nature through a number of extensions – for autogenerating documentation from source code, writing mathematical notation or highlighting source code, etc.
It divided the document into segments, usually sentences, headers, or bullets. Next, it searched all available translation databases for previous human translations of each segment. If any previous human translations of the segment existed, Google Translator Toolkit picked the highest-ranked search result and 'pretranslated' the segment with ...
GNMT improved on the quality of translation by applying an example-based (EBMT) machine translation method in which the system learns from millions of examples of language translation. [2] GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. [ 2 ]
A server that acts as an API front-end, receives API requests, enforces throttling and security policies, passes requests to the back-end service and then passes the response back to the requester. [2] A gateway often includes a transformation engine to orchestrate and modify the requests and responses on the fly. A gateway can also provide ...
The content translation tool assists users in translating existing Wikipedia articles from one language to another. Users select an article in any language, then select another language, and the interface provides machine translation which the human user can then use as inspiration to make readable text in another language.
NiuTrans.SMT is an open-source statistical machine translation system jointly developed by the Natural Language Processing Laboratory of Northeastern University and Shenyang Yayi Network Technology Co., Ltd. NiuTrans.NMT is a lightweight and efficient Transformer-based neural machine translation system.
MateCat ("Machine Translation Enhanced Computer Assisted Translation") is a 3-year research project (Nov 2011 – Oct 2014) funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement No. 287688. [1] It has received over €2,500,000 of European funds. [2]
Like other documentation generators such as Javadoc, Doxygen extracts information from both the comment and the symbolic (non-comment) code. A comment is associated with a programming symbol by immediately preceding it in the code. Markup in the comments allows for controlling inclusion and formatting of the resulting documentation.
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