Technical Brochures And press Releases

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Before the advent of neural networks, translation was done word for word. The system simply translated individual words and sentences, taking into account basic grammar rules. The quality of the translation therefore left something to be desired. However, the smallest elements taken into account by neural systems are not words but fragments. Thanks to this, the machine's computer system does not focus on the position of the words but on the context and meaning of the sentence. The software translates the sentence in its entirety, taking into account its context. It does not store hundreds of translation possibilities in its memory, but works on the semantics of the text and divides the sentences into dictionary segments.

At the moment, GSTAN uses about 32,000 such fragments congo-brazzaville business email list Using special decoders, it determines the importance of each segment of the text. It then calculates the largest number of possible meanings and translation options, before applying the grammar rules to the translated segments. According to the developers, this approach helps ensure high translation speed and accuracy without consuming too much computing power. Semantic and grammatical characteristics specific to languages Given the unique semantic and grammatical characteristics of languages, good translation requires software with completely different algorithms, implemented as separate modules and dictionaries in various programs.

READ ALSO Why should we favor human translation over machine translation? A neural network can work with many language combinations, including those that were not included in the initial training process. Let's imagine a system trained to do translations from English to Japanese and from English to Korean . This one will be able to translate perfectly from Japanese to Korean without using English as an intermediary language. In recent years, artificial intelligence (AI) has developed so much that it can now translate from and into languages ​​for which it was not originally designed. This is because AI has started using its own artificial language, which acts as an intermediate language in the translation process.