A Generalized Reordering Model for Phrase-Based Statistical Machine Translation

Yanqing He, Chengqing Zong


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.
Anthology ID:
2008.amta-papers.10
Volume:
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 21-25
Year:
2008
Address:
Waikiki, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
117–124
Language:
URL:
https://aclanthology.org/2008.amta-papers.10
DOI:
Bibkey:
Cite (ACL):
Yanqing He and Chengqing Zong. 2008. A Generalized Reordering Model for Phrase-Based Statistical Machine Translation. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers, pages 117–124, Waikiki, USA. Association for Machine Translation in the Americas.
Cite (Informal):
A Generalized Reordering Model for Phrase-Based Statistical Machine Translation (He & Zong, AMTA 2008)
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PDF:
https://aclanthology.org/2008.amta-papers.10.pdf