Open AccessString-to-Dependency Statistical Machine Translation
Author(s) -
Libin Shen,
Jinxi Xu,
Ralph Weischedel
Publication year - 2010
Publication title -
computational linguistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.314
H-Index - 98
eISSN - 1530-9312
pISSN - 0891-2017
DOI - 10.1162/coli_a_00015
Subject(s) - computer science , machine translation , dependency (uml) , nist , string (physics) , exploit , language model , artificial intelligence , word (group theory) , natural language processing , algorithm , decoding methods , theoretical computer science , mathematics , geometry , mathematical physics , computer security
We propose a novel string-to-dependency algorithm for statistical machine translation. This algorithm employs a target dependency language model during decoding to exploit long distance word relations, which cannot be modeled with a traditional n-gram language model. Experiments show that the algorithm achieves significant improvement in MT performance over a state-of-the-art hierarchical string-to-string system on NIST MT06 and MT08 newswire evaluation sets.
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