A systematic comparison of various statistical alignment models
Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The Alignment Template Approach to Statistical Machine Translation
Computational Linguistics
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Dependency treelet translation: syntactically informed phrasal SMT
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
SPMT: statistical machine translation with syntactified target language phrases
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Microsoft research treelet translation system: NAACL 2006 Europarl evaluation
StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
Automatic Translation in Two Phases: Recognition and Interpretation
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Syntactic models for structural word insertion and deletion
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Generalizing hierarchical phrase-based translation using rules with adjacent nonterminals
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A decoding method of system combination based on hypergraph in SMT
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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Today's statistical machine translation systems generalize poorly to new domains. Even small shifts can cause precipitous drops in translation quality. Phrasal systems rely heavily, for both reordering and contextual translation, on long phrases that simply fail to match out-of-domain text. Hierarchical systems attempt to generalize these phrases but their learned rules are subject to severe constraints. Syntactic systems can learn lexicalized and unlexicalized rules, but the joint modeling of lexical choice and reordering can narrow the applicability of learned rules. The treelet approach models reordering separately from lexical choice, using a discriminatively trained order model, which allows treelets to apply broadly, and has shown better generalization to new domains, but suffers a factorially large search space. We introduce a new reordering model based on dependency order templates, and show that it outperforms both phrasal and treelet systems on in-domain and out-of-domain text, while limiting the search space.