Neural Machine Translation is a type of machine translation that uses an Artificial Neural Network (ANN) to predict the sequence of a word often in form of a whole sentence, modeling total sentences in a single integrated model. Statistical translation consumes more memory and time whereas the NMT trains its receiver end to end to maximize the performance. NMT is now at the forefront of machine translation outcompeting another traditional translation system.
Neural machine translation contains separately engineered components and it works cohesively to maximize the performance. NMT involves using vectors for words. In this method, each word is transcribed into a particular vector of magnitude and direction. In ordinary language and translation models, separate components are used and NMT uses a single sequence model that produces one word at a time.
A bilateral recurrent neural network called an encoder is used to process a
source sentence to vectors for a second recurrent neural network. This method proves to be efficient and fast compared to ordinary prediction models.
The concept of translation has been used for centuries now. The version of
machine translation used in the 1950s relied on hand-coded rules, the use of
dictionaries, etc. the first recorded display of machine translation happened
in 1954. The international business machine (IBM) handpicked 49 sentences and translated them into English. It had a total of 250 words. It was the first
milestone in the field of translation. In 2016, google introduced NMT to
increase speed and accuracy in translation. Now the google translation is
capable of translating complex sentences of any language accurately and
swiftly. Previously google converted any language to English before translating to the desired language.
Even though Neural Network Translation has advanced so much, it still needs further improvement to match other technologies.
It takes quite some time to learn a new language and do the translation. It is
a subject that is still under research and development. The main challenge is to improve the accuracy and to enhance the ability to learn new languages.
Keeping this in mind it is evident that at this pace NMT will take quite some
time to match the speed and capacity of a human translator.
