The Best Templates Match Technique For Example Based Machine Translation

TL;DR

This paper introduces an improved template matching technique for example-based machine translation, specifically enhancing translation accuracy into Arabic by refining template selection methods.

Contribution

It proposes a modified template matching approach that enhances the effectiveness of example-based machine translation for Arabic, addressing limitations of native methods.

Findings

Improved translation accuracy into Arabic

Enhanced template selection process

Demonstrated effectiveness of the new approach

Abstract

It has been proved that large scale realistic Knowledge Based Machine Translation applications require acquisition of huge knowledge about language and about the world. This knowledge is encoded in computational grammars, lexicons and domain models. Another approach which avoids the need for collecting and analyzing massive knowledge, is the Example Based approach, which is the topic of this paper. We show through the paper that using Example Based in its native form is not suitable for translating into Arabic. Therefore a modification to the basic approach is presented to improve the accuracy of the translation process. The basic idea of the new approach is to improve the technique by which template-based approaches select the appropriate templates.