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For example, if a system was trained to translate between English and Japanese, and English and Korean, then it can easily translate from Japanese to Korean without using English as an intermediate language. A neural network can work with many pairs of languages, including those that were not involved in the initial learning process.
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According to the developers, this approach enables to ensure high speed and accuracy of translation without consuming excessive computational power.ĭue to the semantic and grammatical features of languages, proper translation requires completely different software algorithms which are implemented as separate modules and dictionaries in some programs. The last step is to combine the translated segments with grammar. Next, it computes the maximum number of possible meanings and translation options. By using special decoders, it determines the significance of each segment in the text. Īs of today, GNMT uses about 32,000 such fragments. Instead, it operates on the semantics of the text and divides sentences up into dictionary segments. It does not store hundreds of translation variants in its memory. The software translates the whole sentence by taking into account the context. Thanks to that, the machine’s computational power focuses not on word forms but on the context and meaning of the sentence. In a modern neural system, the smallest element is not a word but its fragments. Therefore, the quality of translation left much to be desired. The system simply translated separate words and phrases, taking into account basic grammar rules. Before the advent of neural networks, translation was usually done in a word-for-word fashion. The neural model of machine translation relies on standard translation methods. Did translation quality really improve and what else does it take to make it even better? How does GT’s algorithm work? Three years have passed since then, and we can now evaluate its effectiveness. With its help, students will be able to locate and buy homework online a lot faster than they did in the past. Based on an artificial neural network, it was meant to improve translation quality immensely. In 2016, Google developers introduced Neural Machine Translation System (GNMT). The first steps in this direction have already been taken, and we’re now seeing algorithms capable of analysing video and audio being actively developed. Hence, we should expect rapid progress in the development of machine translation. Experts claim that GT’s neural system will soon be able to process not only texts but also audio and video files. It supports 103 languages, 10 thousand language pairs, and processes about 500 million translation requests every day. Google Translate (GT) is the world’s number one translation software. Advantages and disadvantages of Google Translate