A Comparison of Various Types of Extended Lexicon Models for Statistical Machine Translation

In this work we give a detailed comparison of the impact of the integration of discriminative and trigger-based lexicon models in state-ofthe-art hierarchical and conventional phrasebased statistical machine translation systems. As both types of extended lexicon models can grow very large, we apply certain restrictions to discard some of the less useful information. We show how these restrictions facilitate the training of the extended lexicon models. We finally evaluate systems that incorporate both types of models with different restrictions on a large-scale translation task for the ArabicEnglish language pair. Our results suggest that extended lexicon models can be substantially reduced in size while still giving clear improvements in translation performance.