@inproceedings{4caf4dfb78044738b3d247628a0bc89b,
title = "A localized prediction model for statistical machine translation",
abstract = "In this paper, we present a novel training method for a localized phrase-based prediction model for statistical machine translation (SMT). The model predicts blocks with orientation to handle local phrase re-ordering. We use a maximum likelihood criterion to train a log-linear block bigram model which uses realvalued features (e.g. a language model score) as well as binary features based on the block identities themselves, e.g. block bigram features. Our training algorithm can easily handle millions of features. The best system obtains a 18.6\% improvement over the baseline on a standard Arabic-English translation task.",
author = "Christoph Tillmann and Tong Zhang",
year = "2005",
doi = "10.3115/1219840.1219909",
language = "English (US)",
isbn = "1932432515",
series = "ACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "557--564",
booktitle = "ACL-05 - 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
address = "United States",
note = "43rd Annual Meeting of the Association for Computational Linguistics, ACL-05 ; Conference date: 25-06-2005 Through 30-06-2005",
}