English-Arabic statistical machine translation: State of the art

Ebrahim S. ; Hegazy, Doaa ; Mostafa M. ; El-Beltagy S. 


© Springer International Publishing Switzerland 2015. This paper presents state of the art of the statistical methods that enhance English to Arabic (En-Ar) Machine Translation (MT). First, the paper introduces a brief history of the machine translation by clarifying the obstacles it faced; as exploring the history shows that research can develop new ideas. Second, the paper discusses the Statistical Machine Translation (SMT) method as an effective state of the art in the MT field. Moreover, it presents the SMT pipeline in brief and explores the En-Ar MT enhancements that have been applied by processing both sides of the parallel corpus before, after and within the pipeline. The paper explores Arabic linguistic challenges in MT such as: orthographic, morphological and syntactical issues. The purpose of surveying only En- Ar translation direction in the SMT is to help transferring the knowledge and science to the Arabic language and spreading the information to all who are interested in the Arabic language.

Other data

Issue Date 1-Jan-2015
Journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 
URI http://research.asu.edu.eg/123456789/1031
ISBN 9783319181103
DOI http://api.elsevier.com/content/abstract/scopus_id/84942627451

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