Neural Machine Translation - NMT
What is Neural Machine Translation - NMT / MT?
Neural Machine Translation - NMT / MT is a machine translation approach that applies a large Artificial Neural Network - ANN toward predicting the likelihood of a sequence of words, often in the form of whole sentences. Unlike Statistical Machine Translation - SMT, 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 out-competing traditional forms of translation systems.
How does Neural Machine Translation - NMT / MT 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 - RNN, 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.
Statistical Machine Translation Models - SMT
Statistical Machine Translation uses Predictive Algorithms to teach a computer how to translate text. These models are created, or learned, from parallel bilingual text corpora and used to create the most probable output, based on different bilingual examples. Using this already translated text, a statistical model guesses or predicts how to translate foreign language text. SMT has different subgroups, including word-based, phrase-based, syntax-based and hierarchical phrase-based.
The benefit of SMT is its automation. One drawback is that this system needs bilingual material to work from, and it can be hard to find content for obscure languages. SMT is a “rule-based” MT method, using the basis of corpora translations to create its own text segments.
Neural Machine Translation - NMT / MT has its own uses and brings a variety of benefits in comparison to SMT, including the following.
- NMT is the newest method of MT and is said to create much more accurate translations than SMT.
- NMT is based on the model of neural networks in the human brain, with information being sent to different “layers” to be processed before output.
- NMT uses deep learning techniques to teach itself to translate text based on existing statistical models. It makes for faster translations than the statistical method and has the ability to create higher quality output.
- NMT is able to use algorithms to learn linguistic rules on its own from statistical models. The biggest benefit to NMT is its speed and quality.
- NMT is said by many to be the way of the future, and the process will no doubt continue to advance in its capabilities.
Applications of Neural Network Translation:
One of the most popular translation machines in the world is Google Translate. The system uses Google Neural Machine Translation to increase its fluency and accuracy. The system not only applies a large data set for training its algorithms, its end-to-end design allows the system to learn over time and create better, more natural translations. Google Neural Machine Translation can even process what are called "Zero-Shot Translations." For example, the translation from French to Spanish is a Zero-Shot translation because it is a direct translation. Previously, Google Translate would translate the initial language into English, and then translate that English to the target language.