Machine Translation Thesis

Machine Translation Thesis-39
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.With respect to MT, it remains an open fundamental issue and one of the most important stages in the life cycle of an MT system.

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.

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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.

On the basis of these findings, a number of suggestions and recommendations are made.

Key words: Machine translation, evaluation of MT systems , black-box evaluation , taskoriented testing , benchmark testing , functional criteria , non-functional / computational criteria , operational problems.

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.

These systems have been tested under experimental conditions by two evaluators.

Detailed analyses and classification of the results concerning the selected criteria are presented with Excel tables, charts and graphs.

The sample represents a total of 268 English sentences taken from twelve various specialdomain texts.

Some computational criteria have also been evaluated.

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