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.
Semantic Roles for SMT: A Hybrid Two-Pass Model
Published 2009 in North American Chapter of the Association for Computational Linguistics
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- Publication year
2009
- Venue
North American Chapter of the Association for Computational Linguistics
- Publication date
2009-05-31
- Fields of study
Computer Science
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