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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
arts and humanities
Methods for integrating rule-based and statistical systems for Arabic to English machine translation
Machine Translation, Volume 26, No. 1-2, Year 2012
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Description
This article presents several techniques for integrating information from a rule-based machine translation (RBMT) system into a statistical machine translation (SMT) framework. These techniques are grouped into three parts that correspond to the type of information integrated: the morphological, lexical, and system levels. The first part presents techniques that use information from a rule-based morphological tagger to do morpheme splitting of the Arabic source text. We also compare with the results of using a statistical morphological tagger. In the second part, we present two ways of using Arabic diacritics to improve SMT results, both based on binary decision trees. The third part presents a system combination method that combines the outputs of the RBMT and the SMT systems, leveraging the strength of each. This article shows how language specific information obtained through a deterministic rule-based process can be used to improve SMT, which is mostly language-independent. © 2011 Springer Science+Business Media B.V.(outside the USA).
Authors & Co-Authors
Zbib, Rabih
United States, Cambridge
Massachusetts Institute of Technology
Kayser, Michael
United States, Cambridge
Bbn Technologies
Matsoukas, Spyros
United States, Cambridge
Bbn Technologies
Makhoul, John
United States, Cambridge
Bbn Technologies
Nader, Hazem
Egypt, Cairo
Sakhr Software
Soliman, Hamdy
Egypt, Cairo
Sakhr Software
Safadi, Rami
Egypt, Cairo
Sakhr Software
Statistics
Citations: 7
Authors: 7
Affiliations: 3
Identifiers
Doi:
10.1007/s10590-011-9106-9
ISSN:
09226567