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A binary-to-text encoding is encoding of data in plain text.More precisely, it is an encoding of binary data in a sequence of printable characters.These encodings are necessary for transmission of data when the communication channel does not allow binary data (such as email or NNTP) or is not 8-bit clean.
As of 2018, all supported memoQ editions contained these principal modules: File statistics Word counts and comparisons with translation memory databases, internal content similarities and format tag frequency. memoQ was the first translation environment tool to enable the weighting of format tags in its count statistics to enable the effort involved with their correct placement in translated ...
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.
^ The current default format is binary. ^ The "classic" format is plain text, and an XML format is also supported. ^ Theoretically possible due to abstraction, but no implementation is included. ^ The primary format is binary, but text and JSON formats are available. [8] [9]
Rosetta is a dynamic binary translator developed by Apple Inc. for macOS, an application compatibility layer between different instruction set architectures. It enables a transition to newer hardware, by automatically translating software. The name is a reference to the Rosetta Stone, the artifact which enabled translation of Egyptian ...
This category lists various binary-to-text encoding formats and standards. Pages in category "Binary-to-text encoding formats" The following 19 pages are in this category, out of 19 total.
Moses is a statistical machine translation engine that can be used to train statistical models of text translation from a source language to a target language, developed by the University of Edinburgh. [2] Moses then allows new source-language text to be decoded using these models to produce automatic translations in the target
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.