Tree-based Hybrid Machine Translation

I present an automatic post-editing approach that combines translation systems which produce syntactic trees as output. The nodes in the generation tree and targetside SCFG tree are aligned and form the basis for computing structural similarity. Structural similarity computation aligns subtrees and based on this alignment, subtrees are substituted to create more accurate translations. Two different techniques have been implemented to compute structural similarity: leaves and tree-edit distance. I report on the translation quality of a machine translation (MT) system where both techniques are implemented. The approach shows significant improvement over the baseline for MT systems with limited training data and structural improvement for MT systems trained on Europarl.