Arabic-English Semantic Word Class Alignment to Improve Statistical Machine Translation

Clustering words is a widely used technique in statistical natural language processing. It requires syntactic, semantic, and contextual features. Especially, semantic clustering is gaining a lot of interest. It consists in grouping a set of words expressing the same idea or sharing the same semantic properties. In this paper, we present a new method to integrate semantic classes in a Statistical Machine Translation (SMT) context to improve the Arabic-English translation quality. In our method, we first apply a semantic word clustering algorithm for English. We then project the obtained semantic word classes from the English side to the Arabic side. This projection is based on available word alignments provided by the alignment step using GIZA++ tool. Finally, we apply a new process to incorporate semantic classes in order to improve the SMT quality. The experimental results show that introducing semantic word classes achieves 4 % of relative improvement on the BLEU score for the Arabic → English translation task.