Combination of arabic preprocessing schemes for statistical machine translation

Fatiha Sadat, Nizar Habash

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Statistical machine translation is quite robust when it comes to the choice of input representation. It only requires consistency between training and testing. As a result, there is a wide range of possible preprocessing choices for data used in statistical machine translation. This is even more so for morphologically rich languages such as Arabic. In this paper, we study the effect of different word-level preprocessing schemes for Arabic on the quality of phrase-based statistical machine translation. We also present and evaluate different methods for combining preprocessing schemes resulting in improved translation quality.

Original languageEnglish (US)
Title of host publicationCOLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Pages1-8
Number of pages8
StatePublished - 2006
Event21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, NSW, Australia
Duration: Jul 17 2006Jul 21 2006

Publication series

NameCOLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume1

Other

Other21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006
Country/TerritoryAustralia
CitySydney, NSW
Period7/17/067/21/06

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Fingerprint

Dive into the research topics of 'Combination of arabic preprocessing schemes for statistical machine translation'. Together they form a unique fingerprint.

Cite this