{"id":1699,"date":"2022-01-31T20:00:32","date_gmt":"2022-01-31T14:30:32","guid":{"rendered":"https:\/\/www.ulatus.com\/translation-blog\/?p=1699"},"modified":"2024-12-02T20:09:18","modified_gmt":"2024-12-02T14:39:18","slug":"a-gentle-introduction-to-neural-machine-translation","status":"publish","type":"post","link":"https:\/\/www.ulatus.com\/translation-blog\/a-gentle-introduction-to-neural-machine-translation\/","title":{"rendered":"A Gentle Introduction to Neural Machine Translation"},"content":{"rendered":"

A Gentle Introduction to Neural Machine Translation<\/strong><\/h2>\n

As more companies launch their products across the globe, the need for high-quality translation services continues to grow exponentially. To meet the evolving and expanding demand for translations,\u00a0neural machine translation (NMT)<\/a>\u00a0has increased in popularity, particularly because of its ability to meet project requirements at a reasonable cost. With its capability to translate based on context, NMT is able to produce top-notch content that is similar to the output of a human translator.<\/p>\n

What is Neural Machine Translation?<\/strong><\/h2>\n

NMT is a method of\u00a0machine translation\u00a0<\/a>that utilizes an\u00a0artificial neural network<\/a>\u00a0to calculate the probability of a series of words, usually modeling full sentences into one integrated unit. During NMT, the neural network encodes and decodes the source text instead of following a predefined set of rules that was put together before the translation began. As a result, NMT can successfully produce contextually accurate translations, while avoiding the issue of translating fragments just one word at a time. Ultimately, NMT is capable of avoiding the problems that accompany standard phrase-based translation systems, resulting in significantly more accurate\u00a0translations<\/a>.<\/p>\n

How Does NMT Work?<\/strong><\/h2>\n

NMT is intended to simulate the neurons of a human brain; as such, the machine\u2019s neurons can collect and learn, make connections across data, and assess input as a complete unit. During the encoding process, the source language\u2019s text is assembled into the\u00a0machine<\/a>, then it is arranged into language vectors. Words and phrases that are contextually similar will be added to comparable vectors. Once that phase is complete, the next step is the decoding phase, during which the vectors are translated into the target language. Because the technology is able to relate words and understand how their meanings are similar, it is translating complete sentences, not simply words.<\/p>\n

NMT\u2019s Benefits Have Helped it Gain Importance\u00a0<\/strong><\/h2>\n

NMT is known for having many benefits that have fueled its growth through the years. These benefits include its ability to:<\/p>\n