A systematic comparison of various statistical alignment models
Computational Linguistics
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
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
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
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Statistical Machine Translation
Statistical Machine Translation
Effects of empty categories on machine translation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Automatic evaluation of translation quality for distant language pairs
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Reordering constraint based on document-level context
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Training a parser for machine translation reordering
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
HPSG-Based Preprocessing for English-to-Japanese Translation
ACM Transactions on Asian Language Information Processing (TALIP)
PLUTO: automated solutions for patent translation
EACL 2012 Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)
Akamon: an open source toolkit for tree/forest-based statistical machine translation
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Post-ordering by parsing for Japanese-English statistical machine translation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Inducing a discriminative parser to optimize machine translation reordering
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Head finalization reordering for Chinese-to-Japanese machine translation
SSST-6 '12 Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation
Zero pronoun resolution can improve the quality of J-E translation
SSST-6 '12 Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation
Natural Language Engineering
Syntax-Based Post-Ordering for Efficient Japanese-to-English Translation
ACM Transactions on Asian Language Information Processing (TALIP)
Post-Ordering by Parsing with ITG for Japanese-English Statistical Machine Translation
ACM Transactions on Asian Language Information Processing (TALIP)
Distortion Model Based on Word Sequence Labeling for Statistical Machine Translation
ACM Transactions on Asian Language Information Processing (TALIP)
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English is a typical SVO (Subject-Verb-Object) language, while Japanese is a typical SOV language. Conventional Statistical Machine Translation (SMT) systems work well within each of these language families. However, SMT-based translation from an SVO language to an SOV language does not work well because their word orders are completely different. Recently, a few groups have proposed rule-based preprocessing methods to mitigate this problem (Xu et al., 2009; Hong et al., 2009). These methods rewrite SVO sentences to derive more SOV-like sentences by using a set of handcrafted rules. In this paper, we propose an alternative single reordering rule: Head Finalization. This is a syntax-based preprocessing approach that offers the advantage of simplicity. We do not have to be concerned about part-of-speech tags or rule weights because the powerful Enju parser allows us to implement the rule at a general level. Our experiments show that its result, Head Final English (HFE), follows almost the same order as Japanese. We also show that this rule improves automatic evaluation scores.