We present a translation model enriched with shallow syntactic and semantic information about the source language. Base-phrase labels and semantic role labels are incorporated into an hierarchical model by creating shallow semantic "trees". Results show an increase in performance of up to 6% in BLEU scores for English-Spanish translation over a standard phrase-based SMT baseline.
REF used semantic roles and base-phrase tags to create shallow semantic trees.
Shallow Semantic Trees for SMT
Proceedings of the Sixth Workshop on Statistical Machine Translation
Computer Science