Extending a model for ontology-based Arabic-English machine translation

TL;DR

This paper enhances an ontology-based Arabic-English machine translation model called NAN, using semantic web techniques to improve translation accuracy and semantic similarity to human translation, especially for non-native speakers.

Contribution

It extends the NAN model with semantic web techniques to better handle homographs and homonyms, improving translation quality over existing instant translators.

Findings

NAN translation is more similar to human translation.

Semantic extension improves translation accuracy.

Helps non-native speakers understand translations better.

Abstract

The acceleration in telecommunication needs leads to many groups of research, especially in communication facilitating and Machine Translation fields. While people contact with others having different languages and cultures, they need to have instant translations. However, the available instant translators are still providing somewhat bad Arabic-English Translations, for instance when translating books or articles, the meaning is not totally accurate. Therefore, using the semantic web techniques to deal with the homographs and homonyms semantically, the aim of this research is to extend a model for the ontology-based Arabic-English Machine Translation, named NAN, which simulate the human way in translation. The experimental results show that NAN translation is approximately more similar to the Human Translation than the other instant translators. The resulted translation will help getting the translated texts in the target language somewhat correctly and semantically more similar to human translations for the Non-Arabic Natives and the Non-English natives.