Reducing Parameter Space for Word Alignment

Hervé Déjean, Éric Gaussier, Cyril Goutte, Kenji Yamada

Abstract: This paper presents the experimental results of our attemps to reduce the size of the parameter space in word alignment algorithm. We use IBM Model 4 as a baseline. In order to reduce the parameter space, we pre-processed the training corpus using a word lemmatizer and a bilingual term extraction algorithm. Using these additional components, we obtained an improvement in the alignment error rate.