Abstract
Problem statement: Any Arabic to English Machine Translation (MT) system should be capable of dealing with word order which Arabic exhibits. This poses a significant challenge to MT due to the vast number of ways to express the same sentence in Arabic. The ordering features are very important and should be carefully applied to ensure the generation of sentence in the target language. Because they apply to the target language, it shoul d fulfill the specific requirement of this language . Mistakes in the MT output can be either the result of analysis problems at the source language level, or due to generation problem at target language level. Word order rules are crucial for the generation of sentences in the target language. They also serve a s rules for the ordering of sentence constituents. These rules draw their information from the syntact ic knowledge. The word order problem becomes more obvious when making machine translation between languages that have rich morphological variations. Approach: The main objective of this research is to develop a machine translation that translates Arabic noun phrases into English by usin g transfer-based approach. A system called Npae- Rbmt has been developed in this research. Transfer- based machine translation is one instance of rule- based machine-translation approaches and is current ly one of the most widely used methods of machine translation. The idea of transfer-based mac hine translation it is necessary to have an intermediate representation that captures the mean ing of the original sentence in order to generate the correct translation. Using advantages of transf er-based machine translation such as analysis step, the Transfer-based becomes simpler as linguistic an alysis goes deeper-as the representation of analysi s step becomes more abstract. In fact, a major goal o f MT research is to define a level of analysis whic h is so deep in which transfer-based machine translat ion is able to do. Results: The method was tested on 88 thesis titles and journals from the computer science domain. The accuracy of the result was 94.6%. These results proved the viability of this a pproach for distant languages. Conclusion: Based on the achieved results, we have managed to perform th e syntactic reordering within an Arabic noun phrases to English translation task by using transf er-based machine translation and also achieved reasonable improvements in translation quality over related approach.
Highlights
Machine Translation (MT) is formally defined as the use of a computer to translate a message, typically text or speech, from one natural language to another (Salem and Nolan, 2009)
Machine translation system develops by using four approaches depending on their difficulty and complexity
Rule-based machine translation approaches can be classified into the following categories: direct machine translation, interlingua machine translation and transferbased machine translation (Abu Shquier and Sembok, 2008)
Summary
Machine Translation (MT) is formally defined as the use of a computer to translate a message, typically text or speech, from one natural language to another (Salem and Nolan, 2009). Machine translation system develops by using four approaches depending on their difficulty and complexity. Transfer-based machine translation: One of the main features of transfer based machine translation systems is a phase that “transfers” an intermediate representation of the text in the original language to an intermediate representation of text in the target language (Shaalan et al, 2004). This can work at one of two levels of linguistic analysis, or somewhere in between.
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