@inproceedings{he-zong-2008-generalized,
title = "A Generalized Reordering Model for Phrase-Based Statistical Machine Translation",
author = "He, Yanqing and
Zong, Chengqing",
booktitle = "Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers",
month = oct # " 21-25",
year = "2008",
address = "Waikiki, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2008.amta-papers.10",
pages = "117--124",
abstract = "Phrase-based translation models are widely studied in statistical machine translation (SMT). However, the existing phrase-based translation models either can not deal with non-contiguous phrases or reorder phrases only by the rules without an effective reordering model. In this paper, we propose a generalized reordering model (GREM) for phrase-based statistical machine translation, which is not only able to capture the knowledge on the local and global reordering of phrases, but also is able to obtain some capabilities of phrasal generalization by using non-contiguous phrases. The experimental results have indicated that our model out- performs MEBTG (enhanced BTG with a maximum entropy-based reordering model) and HPTM (hierarchical phrase-based translation model) by improvement of 1.54{\%} and 0.66{\%} in BLEU.",
}
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%0 Conference Proceedings
%T A Generalized Reordering Model for Phrase-Based Statistical Machine Translation
%A He, Yanqing
%A Zong, Chengqing
%S Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2008
%8 oct 21 25
%I Association for Machine Translation in the Americas
%C Waikiki, USA
%F he-zong-2008-generalized
%X Phrase-based translation models are widely studied in statistical machine translation (SMT). However, the existing phrase-based translation models either can not deal with non-contiguous phrases or reorder phrases only by the rules without an effective reordering model. In this paper, we propose a generalized reordering model (GREM) for phrase-based statistical machine translation, which is not only able to capture the knowledge on the local and global reordering of phrases, but also is able to obtain some capabilities of phrasal generalization by using non-contiguous phrases. The experimental results have indicated that our model out- performs MEBTG (enhanced BTG with a maximum entropy-based reordering model) and HPTM (hierarchical phrase-based translation model) by improvement of 1.54% and 0.66% in BLEU.
%U https://aclanthology.org/2008.amta-papers.10
%P 117-124
Markdown (Informal)
[A Generalized Reordering Model for Phrase-Based Statistical Machine Translation](https://aclanthology.org/2008.amta-papers.10) (He & Zong, AMTA 2008)
ACL