Abstract
This research presents an approach for an English-to-Arabic Machine Translation System based on Building correct grammar and phrase structures first and then automatically deriving Translation Rules for phrase translation. For every English phrase, the grammar is first analysed and then a corresponding Arabic translation is given which would be used by the machine learning system to produce a translation rule with the help of a dictionary and the user. These same derived rules can partially be used for other phrase sequences especially in the case of a phrase consisting of a number of smaller phrases and thus implemeting the idea of recusive phrase strucutres. The approach was implemented and tested on simple cases and the results are given which indicate that this approach is successful for small to medium phrases. Our approach is an enhancement on existing phrase translation techniques because it analyses the source language grammar first, then builds a syntactic structure before proceeding with the machine learning process of learning the translation rules. Our approach is enhancement on existing phrase based translations in two directions: the grammar editing before the translation rules and the derived translation rules can be complete for complete phrases or are rules for translatioing smaller phrases which are subsets or larger phrases. The approach has improved the spped and correctness of phrase translations.
Highlights
Machine Translation techniques have recently achieved some success but in spite of this success, MT still has many hurdles to overcome
This study presented an approach for English-toArabic machine translation
The approach was based on training the TARJIM system to accept English phrases, their structures, their Arabic translation and automatically derive the translation rules
Summary
Machine Translation techniques have recently achieved some success but in spite of this success, MT still has many hurdles to overcome. Our approach as implemented in TARJEM can understand the meaning of the word by putting it in a grammar sequence and by learning more examples from the user. TARJIM derives the semantics translation rules from the example based concept of previous instances of sentences and this puts the source and target language sentences in grammar context, semantic context and in syntactic context.
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