A Cocktail of Deep Syntactic Features for Hierarchical Machine Translation.

In this work we review and compare three additional syntactic enhancements for the hierarchical phrase-based translation model, which have been presented in the last few years. We compare their performance when applied separately and study whether the combination may yield additional improvements. Our findings show that the models are complementary, and their combination achieve an increase of 1 % in BLEU and a reduction of nearly 2 % in TER. The models presented in this work are made available as part of the Jane open source machine translation toolkit. 1