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In 2019, ProMT introduced its new neural technology [6] and flagship solution - PROMT Neural Translation Server. [7] Since then all MT systems developed by ProMT are based on neural machine translation. The software can run on Microsoft Windows, Linux, MacOS, iOS and Android and works in offline mode providing secure machine translation.
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.
When the online service was first introduced, the head of Yandex.Translate, Alexei Baitin, stated that although machine translation cannot be compared to a literary text, the translations produced by the system can provide a convenient option for understanding the general meaning of the text in a foreign language. [8]
A DMT system is designed for a specific source and target language pair and the translation unit of which is usually a word. Translation is then performed on representations of the source sentence structure and meaning respectively through syntactic and semantic transfer approaches. A transfer-based machine translation system involves three ...
Reverso's suite of online linguistic services has over 96 million users, and comprises various types of language web apps and tools for translation and language learning. [11] Its tools support many languages, including Arabic, Chinese, English, French, Hebrew, Spanish, Italian, Turkish, Ukrainian and Russian.
In the translation process, it can display statistics for the body of translations hosted by the server and allow users to make translation suggestions and corrections for later review. It acts as a translation-specific bug reporting system, allowing online translation with various translators, operating as a management system where translators ...
By 2020, the system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder. [10] 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]
This approach involves serially processing the input multiple times. The most common technique used in multi-pass machine translation systems is to pre-process the input with a rule-based machine translation system. The output of the rule-based pre-processor is passed to a statistical machine translation system, which produces the final output ...