BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Hierarchical Phrase-Based Translation
Computational Linguistics
A systematic comparison of phrase-based, hierarchical and syntax-augmented statistical MT
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Rule filtering by pattern for efficient hierarchical translation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Discarding monotone composed rule for hierarchical phrase-based statistical machine translation
Proceedings of the 3rd International Universal Communication Symposium
A Gibbs sampler for phrasal synchronous grammar induction
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Learning stochastic bracketing inversion transduction grammars with a cubic time biparsing algorithm
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Hierarchical phrase-based translation with weighted finite-state transducers and shallow-n grammars
Computational Linguistics
Bayesian extraction of minimal SCFG rules for hierarchical phrase-based translation
WMT '11 Proceedings of the Sixth Workshop on Statistical Machine Translation
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Shallow-n grammars (de Gispert et al., 2010) were introduced to reduce over-generation in the Hiero translation model (Chiang, 2005) resulting in much faster decoding and restricting reordering to a desired level for specific language pairs. However, Shallow-n grammars require parameters which cannot be directly optimized using minimum error-rate tuning by the decoder. This paper introduces some novel improvements to the translation model for Shallow-n grammars. We introduce two rules: a BITG-style reordering glue rule and a simpler monotonic concatenation rule. We use separate features for the new rules in our log-linear model allowing the decoder to directly optimize the feature weights. We show this formulation of Shallow-n hierarchical phrase-based translation is comparable in translation quality to full Hiero-style decoding (without shallow rules) while at the same time being considerably faster.