Context-Dependent Alignment Models for Statistical Machine Translation

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Context-Dependent Alignment Models for Statistical Machine Translation” by J. Brunning, A. de Gispert, and W. Byrne. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2009, pp. 110-118 (9 pages).

Abstract

We introduce alignment models for Machine Translation that take into account the context of a source word when determining its translation. Since the use of these contexts alone causes data sparsity problems, we develop a decision tree algorithm for clustering the contexts based on optimisation of the EM auxiliary function. We show that our context-dependent models lead to an improvement in alignment quality, and an increase in translation quality when the alignments are used to build a machine translation system.

Download: slides.

BibTeX entry:

@inproceedings{brunning09:hlt,
   author = {J. Brunning and A. de Gispert and W. Byrne},
   title = {Context-Dependent Alignment Models for Statistical Machine
	Translation},
   booktitle = {Proceedings of Human Language Technologies: The 2009
	Annual Conference of the North American Chapter of the Association
	for Computational Linguistics},
   pages = {110--118 (9 pages)},
   year = {2009},
   url = {http://www.aclweb.org/anthology/N/N09/N09-1013.pdf}
}

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