We present results on a novel hybrid semantic SMT model that incorporates the strengths of both semantic role labeling and phrase-based statistical machine translation. The approach avoids major complexity limitations via a two-pass architecture. The first pass is performed using a conventional phrase-based SMT model. The second pass is performed by a re-ordering strategy guided by shallow semantic parsers that produce both semantic frame and role labels. Evaluation on a Wall Street Journal newswire genre test set showed the hybrid model to yield an improvement of roughly half a point in BLEU score over a strong pure phrase-based SMT baseline -to our knowledge, the first successful application of semantic role labeling to SMT.
REF present a two-pass model to incorporate semantic information to the phrase-based SMT pipeline.
Semantic Roles for SMT: A Hybrid Two-Pass Model
HLT-NAACL, Companion Volume: Short Papers
Computer Science