A systematic comparison of various statistical alignment models
Computational Linguistics
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
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
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Hierarchical Phrase-Based Translation
Computational Linguistics
Using a maximum entropy model to build segmentation lattices for MT
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
Findings of the 2011 Workshop on Statistical Machine Translation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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This paper presents the system we developed for the 2011 WMT Haitian Creole--English SMS featured translation task. Applying standard statistical machine translation methods to noisy real-world SMS data in a low-density language setting such as Haitian Creole poses a unique set of challenges, which we attempt to address in this work. Along with techniques to better exploit the limited available training data, we explore the benefits of several methods for alleviating the additional noise inherent in the SMS and transforming it to better suite the assumptions of our hierarchical phrase-based model system. We show that these methods lead to significant improvements in BLEU score over the baseline.