@inproceedings{7af56611d00a415ea1afd497dd2667ec,
title = "Alignment symmetrization optimization targeting phrase pivot statistical machine translation",
abstract = "An important step in mainstream statistical machine translation (SMT) is combining bidirectional alignments into one alignment model. This process is called symmetrization. Most of the symmetrization heuristics and models are focused on direct translation (source-to-target). In this paper, we present symmetrization heuristic relaxation to improve the quality of phrase-pivot SMT (source-[pivot]-target). We show positive results (1.2 BLEU points) on Hebrew-to-Arabic SMT pivoting on English.",
author = "{El Kholy}, Ahmed and Nizar Habash",
note = "Funding Information: The work presented in this paper was funded by a Google research award. We would like to thank Reshef Shilon for helpful feedback and discussions. We also thank the anonymous reviewers for their insightful comments. Publisher Copyright: {\textcopyright} 2014 The authors.; 17th Annual Conference of the European Association for Machine Translation, EAMT 2014 ; Conference date: 16-06-2014 Through 18-06-2014",
year = "2014",
language = "English (US)",
series = "Proceedings of the 17th Annual Conference of the European Association for Machine Translation, EAMT 2014",
publisher = "European Association for Machine Translation",
pages = "63--70",
editor = "Marko Tadic and Philipp Koehn and Philipp Koehn and Andy Way and Johann Roturier",
booktitle = "Proceedings of the 17th Annual Conference of the European Association for Machine Translation, EAMT 2014",
}