Efficient Solutions for Word Reordering in German-English Phrase-Based Statistical Machine Translation

Despite being closely related languages, German and English are characterized by important word order differences. Longrange reordering of verbs, in particular, represents a real challenge for state-of-theart SMT systems and is one of the main reasons why translation quality is often so poor in this language pair. In this work, we review several solutions to improve the accuracy of German-English word reordering while preserving the efficiency of phrase-based decoding. Among these, we consider a novel technique to dynamically shape the reordering search space and effectively capture long-range reordering phenomena. Through an extensive evaluation including diverse translation quality metrics, we show that these solutions can significantly narrow the gap between phrase-based and hierarchical SMT.