Until a few decades ago, the word “translator” was associated exclusively with a person. But with the advent of computers, translation began to develop: first in the interests of special services, then science and business. Neural machine translation is machine translation that is based on machine learning technology.

They use parallel corpus analysis and require significant computing power. Such engines are often trained on graphics cards rather than processors. This is the third generation of MP technology.

The first neural engines were introduced to the market in 2016, leading to a leap forward in the quality of machine translation. But the MP still falls short of the human level.

Neural machine translation today offers unprecedented speed and equally unprecedented quality. With the help of modern linguistic solutions, you can get coherent, smooth, and accurate English to Hindi translations in a matter of seconds without human editing. This opportunity is made possible by neural networks.

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What is a neural network for translations?

Neural networks are so named because the system is vaguely inspired by the biological neural networks that make up the human brain. The elements representing neurons are organized into layers (neural network).

  • A network comprises units called “nodes” (for example, human neurons), and all units are connected.
  • Each connection is a number called an “edge.”
  • Neurons and edge connections are represented by a specific number called “weights.”

During processing, the weight is adjusted, for example, when the machine has more translated data to analyze.

How does the translation model work?

The simple answer is with a complex mathematical formula (represented by a neural network). This formula takes a string of numbers as input and outputs the resulting string of numbers.

The parameters of a neural network are actually created and refined during the training of the network with millions of pairs of data (for example, translating a pair of sentences from English and Hindi).

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Neural machine translation uses models

The machine can also be trained to meet the specific needs of the sector (legal translation, medical translation, etc.) or the client’s field of activity, which will have its own vocabulary.

NMT mainly relies on the data used to train neural machine networks as it learns how to simulate the data that it was trained with.

Many highly custom-designed machine translation and accurate industry-specific models still incorporate statistical and neural techniques to achieve maximum performance for their clients.

Each pair of sentences slightly modifies the neural network model as it goes through each pair of sentences using a backpropagation algorithm.

Neural machine translation can learn to translate better

Unlike some older machines still on the market (statistical and rule-based), a neural machine simulates the entire machine translation process using a unique artificial neural network. Google started using its own GMNT back in 2016.

Today, the neural machine is like the human brain and is capable of producing a reliable translation and also learning a language. As such, neural networks continually improve the quality of the translated material. To enhance the performance of the machine, it is “trained” by translators.

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Benefits of automatic neural translation for your website

Connecting Linguise translation to your website will bring all the benefits of automatic neural translation to your clients, mainly:

  • More than 80 languages ​​are available
  • High accuracy of the translation, especially into popular languages
  • Instant translation of the entire site, regardless of platform or content size
  • All neural translations can be edited

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If you’ve recently used a machine translation system like Google Translate, you were probably pleasantly surprised by the quality of the translation.

If earlier the system produced extremely incomprehensible constructions with annoying errors that sometimes cause a smile, sometimes bewilderment, then modern software offers translations that not only correctly convey the content of the source text, but also take into account the style of the language.