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
A hierarchical phrase-based model for statistical machine translation
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
Using dependency order templates to improve generality in translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
An empirical study in source word deletion for phrase-based statistical machine translation
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
MT error detection for cross-lingual question answering
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
ICE-TEA: in-context expansion and translation of English abbreviations
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
A semantic schema - based approach for natural language translation
WSEAS Transactions on Computers
An approach for contextual translation based on semantic schemas
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume I
Statistical machine translation enhancements through linguistic levels: A survey
ACM Computing Surveys (CSUR)
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An important problem in translation neglected by most recent statistical machine translation systems is insertion and deletion of words, such as function words, motivated by linguistic structure rather than adjacent lexical context. Phrasal and hierarchical systems can only insert or delete words in the context of a larger phrase or rule. While this may suffice when translating in-domain, it performs poorly when trying to translate broad domains such as web text. Various syntactic approaches have been proposed that begin to address this problem by learning lexicalized and unlexicalized rules. Among these, the treelet approach uses unlexicalized order templates to model ordering separately from lexical choice. We introduce an extension to the latter that allows for structural word insertion and deletion, without requiring a lexical anchor, and show that it produces gains of more than 1.0% BLEU over both phrasal and baseline treelet systems on broad domain text.