Arabic machine transliteration using an attention-based encoder-decoder model

Conference paper

Hadj Ameur, MS, Meziane, F. and Guessoum, A 2017. Arabic machine transliteration using an attention-based encoder-decoder model. 3rd International Conference on Arabic Computational Linguistics, ACLing 2017. Dubai 05 - 06 Nov 2017 Elseiver. https://doi.org/10.1016/j.procs.2017.10.120
AuthorsHadj Ameur, MS, Meziane, F. and Guessoum, A
TypeConference paper
Abstract

Transliteration is the process of converting words from a given source language alphabet to a target language alphabet, in a way
that best preserves the phonetic and orthographic aspects of the transliterated words. Even though an important effort has been
made towards improving this process for many languages such as English, French and Chinese, little research work has been
accomplished with regard to the Arabic language. In this work, an attention-based encoder-decoder system is proposed for the
task of Machine Transliteration between the Arabic and English languages. Our experiments proved the efficiency of our proposal
approach in comparison to some previous research developed in this area.

KeywordsArabic Language; ; Arabic Transliteration; ; Deep Learning; ; Encoder-decoder Architecture; ; Sequence-to-sequence Models; Natural Language Processing; ; Recurrent Neural Networks;
Year2017
Conference3rd International Conference on Arabic Computational Linguistics, ACLing 2017
JournalProcedia Computer Science
PublisherElseiver
ISSN18770509
Digital Object Identifier (DOI)https://doi.org/10.1016/j.procs.2017.10.120
Web address (URL)http://dx.doi.org/10.1016/j.procs.2017.10.120
Journal citationVol 117, pp. 287 - 297
Web address (URL) of conference proceedingshttps://www.aconf.org/conf_109394
Output statusPublished
Publication dates09 Nov 2017
Publication process dates
Deposited05 Jun 2023
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