@inproceedings{1aa9c62b991f496191d50912a811f110,
title = "QCRI-MES Submission at WMT13: Using Transliteration Mining to Improve Statistical Machine Translation",
abstract = "This paper describes QCRI-MES's submission on the English-Russian dataset to the Eighth Workshop on Statistical Machine Translation. We generate improved word alignment of the training data by incorporating an unsupervised transliteration mining module to GIZA++ and build a phrase-based machine translation system. For tuning, we use a variation of PRO which provides better weights by optimizing BLEU+1 at corpus-level. We transliterate out-of-vocabulary words in a post-processing step by using a transliteration system built on the transliteration pairs extracted using an unsupervised transliteration mining system. For the Russian to English translation direction, we apply linguistically motivated pre-processing on the Russian side of the data.",
author = "Hassan Sajjad and Svetlana Smekalova and Nadir Durrani and Alexander Fraser and Helmut Schmid",
note = "Publisher Copyright: {\textcopyright} 2013 Association for Computational Linguistics; 8th Workshop on Statistical Machine Translation, WMT 2013 ; Conference date: 08-08-2013 Through 09-08-2013",
year = "2013",
language = "English",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "219--224",
editor = "Ondrej Bojar and Christian Buck and Chris Callison-Burch and Barry Haddow and Philipp Koehn and Christof Monz and Matt Post and Herve Saint-Amand and Radu Soricut and Lucia Specia",
booktitle = "WMT 2013 - 8th Workshop on Statistical Machine Translation, Proceedings",
address = "United States",
}