Paper: Context-Dependent Alignment Models for Statistical Machine Translation

ACL ID N09-1013
Title Context-Dependent Alignment Models for Statistical Machine Translation
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors

We introduce alignment models for Machine Trans- lation that take into account the context of a source word when determining its translation. Since the use of these contexts alone causes data sparsity prob- lems, we develop a decision tree algorithm for clus- tering the contexts based on optimisation of the EM auxiliary function. We show that our context- dependent models lead to an improvement in align- ment quality, and an increase in translation quality when the alignments are used in Arabic-English and Chinese-English translation.