A systematic comparison of various statistical alignment models
Computational Linguistics
Computational Linguistics - Special issue on web as corpus
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Reliable measures for aligning Japanese-English news articles and sentences
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improving Machine Translation Performance by Exploiting Non-Parallel Corpora
Computational Linguistics
Improved statistical machine translation using paraphrases
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Revisiting pivot language approach for machine translation
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Can crowds build parallel corpora for machine translation systems?
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Machine translation of Arabic dialects
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Recent studies in Statistical Machine Translation (SMT) paradigm have been focused on developing foreign language to English translation systems. However as SMT systems have matured, there is a lot of demand to translate from one foreign language to another language. Unfortunately, the availability of parallel training corpora for a pair of morphologically complex foreign languages like Arabic and Hebrew is very scarce. This paper uses active learning based data selection and crowd sourcing technique like Amazon Mechanical Turk to create Arabic-Hebrew parallel corpora. It then explores two different techniques to build Arabic-Hebrew SMT system. The first one involves the traditional cascading of two SMT systems using English as a pivot language. The second approach is training a direct Arabic-Hebrew SMT system using sentence pivoting. Finally, we use a phrase generalization approach to further improve our performance.