What is Neural Machine Translation?
Neural Machine Translation is a machine translation approach that applies a large artificial neural network toward predicting the likelihood of a sequence of words, often in the form of whole sentences. Unlike statistical machine translation, which consumes more memory and time, neural machine translation, NMT, trains its parts end-to-end to maximize performance. NMT systems are quickly moving to the forefront of machine translation, recently outcompeting traditional forms of translation systems.
How does Neural Machine Translation work?
As referenced above, unlike traditional methods of machine translation that involve separately engineered components, NMT works cohesively to maximize its performance. Additionally, NMT employs the use of vector representations for words and internal state. This means that words are transcribed into a vector defined by a unique magnitude and direction. Compared to phrase-based models, this framework is much simpler. Rather than separate component like the language model and translation model, NMT uses a single sequence model that produces one word at a time.
The NMT uses a bidirectional recurrent neural network, also called an encoder, to process a source sentence into vectors for a second recurrent neural network, called the decoder, to predict words in the target language. This process, while differing from phrase-based models in method, prove to be comparable in speed and accuracy.
