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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.Machine Translation (MT) is a subfield of computational linguistics that is focused on translating text from one language to another.
Arab World English Journal(October 2012) Theses / Dissertation Abstract PDFFull Paper PDF Evaluation of Machine Translation Systems: The Translation Quality of Three Arabic Systems Name of researcher : Yasmin Hikmet Abdul Hamid Hannouna Title of the thesis/dissertation: Subject/major: Linguistics & Translation University name, department name: Dept.
of Translation, College of Arts, Al Mustansiriyyah University, Iraq Year of award: 2004 Abstract: Evaluation is an implicit aspect of all human activity.
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.
The theoretical construct adopted in this study is based on the Framework for Evaluation of MT in ISLE (FEMTI, 2003) which is the most recent and comprehensive model of evaluation.
The evaluation, constitutes a standard test bed application of this methodology (i.e., task-oriented testing and benchmark testing).The operational problems are attributed to certain impediments in measuring the speed of translation and some limitations relevant to the design and performance of the user dictionaries of these systems.Having identified these problems, the researcher then investigates their possible sources.The proposed model for the functional criteria is a black-box type, comparative and adequacy-oriented evaluation.As for the non-functional criteria, the evaluation model is said to be the comparative performance and adequacy-oriented type.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.Before diving into the Encoder Decoder structure that is oftentimes used as the algorithm in the above figure, we first must understand how we overcome a large hurdle in any machine translation task.Namely, we need a way to transform sentences into a data format that can be inputted into a machine learning model.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.