As opposed to the current engines on the market (statistical and rule-based), a neural engine models the entire process of machine translation through a unique artificial neural network.
An Artificial Neural Network (ANN) is composed of layers of artificial neurons, the layers are connected together with weights called the parameters of the network.
A key element of neural network is in its ability to automatically correct its parameters during the training phase (few weeks). Technically, the generated output is compared to expected reference and corrective feedback sent "backward" to adjust weights and tune the network connections.
This technology which is based on complex algorithms at the forefront of Deep Learning, enables the PNMT™ (Pure Neural™ Machine Translation) engine to learn, generate the rules of a language from a given translated text and produce a translation overachieving the current state of the art and better than a non-native speaker.