Improving phrase-based statistical translation through combination of word alignment (2006)
| Venue: | In Proc. FinTAL |
| Citations: | 5 - 0 self |
BibTeX
@INPROCEEDINGS{Chen06improvingphrase-based,
author = {Boxing Chen and Marcello Federico},
title = {Improving phrase-based statistical translation through combination of word alignment},
booktitle = {In Proc. FinTAL},
year = {2006}
}
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Abstract
Abstract. This paper investigates the combination of word-alignments computed with the competitive linking algorithm and well-established IBM models. New training methods for phrase-based statistical translation are proposed, which have been evaluated on a popular traveling domain task, with English as target language, and Chinese, Japanese, Arabic and Italian as source languages. Experiments were performed with a highly competitive phrase-based translation system, which ranked at the top in the 2005 IWSLT evaluation campaign. By applying the proposed techniques, even under very different data-sparseness conditions, consistent improvements in BLEU and NIST scores were obtained on all considered language pairs. 1
