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- W1978008954 abstract "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." @default.
- W1978008954 created "2016-06-24" @default.
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- W1978008954 date "2008-01-01" @default.
- W1978008954 modified "2023-09-26" @default.
- W1978008954 title "Syntactic models for structural word insertion and deletion" @default.
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- W1978008954 doi "https://doi.org/10.3115/1613715.1613808" @default.
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