Neural machine translation (NMT) is an algorithm used to translate words from one language to another. It is said that high quality NMT can determine the context of the translation and use models to offer a more accurate translation. Google Translate, DeepL, Yandex, Baidu Translate are well-known examples of NMT available to the public via browser automatic translation.
Let’s get deeper into it.
What is a “neural network” for translations?
Neural Networks are called this way because the system is vaguely inspired by the biological neural networks that constitute human brain. Elements that represent the neurons are organized into layers (neural network).
- The network is constituted by units called “nodes” (like human neurons) and all the units are connected
- Each connection is a number that is called an “edge”
- Neurons and edge connection is represented by a specific number called a “weight”
During the processing the weight is adjusted, for example once the machine has more translated data to analyze.
Applied to translation, unlike the traditional phrase-based translation system, which consists of many small sub-components that are tuned separately, neural machine translation attempts to build and train a large neural network that reads a sentence and the context.
How does the translation model work?
The simple answer is via a complex mathematical formula (represented as a neural network). This formula takes in a string of numbers as inputs and outputs a resulting string of numbers. The parameters of this neural network are created and refined via training the network with millions of sentence pairs (e.g. English and Chinese sentence pair translations).
Neural Machine Translation is using models
A machine can also be trained to meet the specific needs of a sector (legal translation, medical translation, etc.) or a customer’s area of activity, which will have its vocabulary.
NMT are primarily dependent on the training data used to train the neural network as it learns to mimic the data it has been trained with. Many high-accuracy industry specific and custom developed machine translation (MT) models still incorporate both neural and statistical methods today to squeeze the best performance for our clients
Each sentence pair modifies the neural network model slightly as it runs through each sentence pair using an algorithm called back-propagation.
Translation model illustration
Neural Machine Translation can learn how to translate better
As opposed to some old engines still on the market (statistical and rule-based), a neural engine models the entire process of machine translation through a unique artificial neural network. Google starts using its own GMNT back in 2016.
Today, the machine – like the human brain – is capable of producing a reliable translation as well as learning a language, and is therefore constantly improving the quality of the translated material. To improve the performance of the machine, it is “trained” by human translators.
Google neural machine translation illustration
In practical terms, the NMT quality depends on
- A huge volume of translated data (words, segments of sentences, and texts already translated)
- The quality of the translation provided to improve the reliability and sophistication of the results
- The power of the computers used to create the models and also in some case to handle real-time translations
Benefits from Neural Automatic Translation for your website
Connecting Linguise translation with your website will bring all the benefits from the automatic neural translation to your customers, mainly:
- 100+ languages available
- High accuracy of the translation, especially in popular languages
- Instant translation of all the website, no matter the platform or the content size