Phrase-based statistical machine translation by using reordering search and additional features

Authors:
Miao Li;Peng Gao;Jian Zhang;Yi Luo
Affiliations:
Institute of Intelligent of Machines, China Academy of Sciences, Anhui, China;Institute of Intelligent of Machines, China Academy of Sciences, Anhui, China;Institute of Intelligent of Machines, China Academy of Sciences, Anhui, China;Institute of Intelligent of Machines, China Academy of Sciences, Anhui, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

Citing 6
Cited 0

A statistical approach to machine translation

Computational Linguistics
A maximum entropy approach to natural language processing

Computational Linguistics
Phrase-Based Statistical Machine Translation

KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
Discriminative training and maximum entropy models for statistical machine translation

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation

NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
The Alignment Template Approach to Statistical Machine Translation

Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The state of the art statistical machine translation (SMT) systems are based on phrase (a group of words), which are modeled using log-linear maximum entropy framework. In this paper, we constructed a phrase-based statistical machine translation system with additional feature models. The translation model is combined with four specific additional feature functions. When comparing our system with the baseline system of IWSLT2005, we can conclude that our system improve the SMT system accuracy with the same corpus.