Improving word alignment using syntactic dependencies
Ma, Yanjun and Ozdowska, Sylwia and Sun, Yanli and Way, Andy (2008) Improving word alignment using syntactic dependencies. In: ACL08-SSST - Proceedings of ACL08 workshop on Syntax and Structure in Statistical Translation, 20 June 2008, Columbus, Ohio, USA..
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We introduce a word alignment framework that facilitates the incorporation of syntax encoded in bilingual dependency tree pairs. Our model consists of two sub-models: an anchor
word alignmentmodel which aims to find a set of high-precision anchor links and a syntax enhanced word alignment model which focuses on aligning the remaining words relying on dependency information invoked by the acquired anchor links. We show that our syntax enhanced
word alignment approach leads to a 10.32% and 5.57% relative decrease in alignment error rate compared to a generative word alignment model and a syntax-proof discriminative word alignment model respectively.
Furthermore, our approach is evaluated extrinsically
using a phrase-based statistical machine translation system. The results show that SMT systems based on our word alignment approach tend to generate shorter outputs.
Without length penalty, using our word alignments yields statistically significant improvement in Chinese–English machine translation in comparison with the baseline word
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