A systematic comparison of various statistical alignment models
Computational Linguistics
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
A new quantitative quality measure for machine translation systems
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Probabilistic disambiguation models for wide-coverage HPSG parsing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
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
Clause restructuring for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Improving a statistical MT system with automatically learned rewrite patterns
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Feature forest models for probabilistic hpsg parsing
Computational Linguistics
Using a dependency parser to improve SMT for subject-object-verb languages
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Improved tree-to-string transducer for machine translation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
A syntax-directed translator with extended domain of locality
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Statistical Machine Translation
Statistical Machine Translation
Fine-grained tree-to-string translation rule extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Head finalization: a simple reordering rule for SOV languages
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Divide and translate: improving long distance reordering in statistical machine translation
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Automatically learning source-side reordering rules for large scale machine translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Constituent reordering and syntax models for English-to-Japanese statistical machine translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Syntax-Based Post-Ordering for Efficient Japanese-to-English Translation
ACM Transactions on Asian Language Information Processing (TALIP)
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Japanese sentences have completely different word orders from corresponding English sentences. Typical phrase-based statistical machine translation (SMT) systems such as Moses search for the best word permutation within a given distance limit (distortion limit). For English-to-Japanese translation, we need a large distance limit to obtain acceptable translations, and the number of translation candidates is extremely large. Therefore, SMT systems often fail to find acceptable translations within a limited time. To solve this problem, some researchers use rule-based preprocessing approaches, which reorder English words just like Japanese by using dozens of rules. Our idea is based on the following two observations: (1) Japanese is a typical head-final language, and (2) we can detect heads of English sentences by a head-driven phrase structure grammar (HPSG) parser. The main contributions of this article are twofold: First, we demonstrate how off-the-shelf, state-of-the-art HPSG parser enables us to write the reordering rules in an abstract level and can easily improve the quality of English-to-Japanese translation. Second, we also show that syntactic heads achieve better results than semantic heads. The proposed method outperforms the best system of NTCIR-7 PATMT EJ task.