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The sample represents a total of 268 English sentences taken from twelve various specialdomain texts.Some computational criteria have also been evaluated.
In essence, we must somehow convert our textual data into a numeric form.
To do this in machine translation, each word is transformed into a One Hot Encoding vector which can then be inputted into the model.
While Google Translate is the leading industry example of NMT, tech companies all over the globe are going all in on NMT.
This state-of-the-art algorithm is an application of deep learning in which massive datasets of translated sentences are used to train a model capable of translating between any two languages.
The overall comparison of the three systems in terms of quality assessment of both criteria and texts level confirm that English-into-Arabic MT systems suffer from serious drawbacks especially related to the grammar and meanings of the translated sentence.
Their output reflects many deficiencies in translating various text types and they all need serious improvements.
Following this, the latter part of this article provides a tutorial which will allow the chance for you to create one of these structures yourself.
This code tutorial is based largely on the Py Torch tutorial on NMT with a number of enhancements.
Most notably, this code tutorial can be run on a GPU to receive significantly better results.
Before we begin, it is assumed that if you are reading this article you have at least a general knowledge of neural networks and deep learning; particularly the ideas of forward-propagation, loss functions and back-propagation, and the importance of train and test sets.