Re-structuring, Re-labeling, and Re-aligning for Syntax-Based Machine Translation
نویسندگان
چکیده
منابع مشابه
Re-structuring, Re-labeling, and Re-aligning for Syntax-Based Machine Translation
This article shows that the structure of bilingual material from standard parsing and alignment tools is not optimal for training syntax-based statistical machine translation (SMT) systems. We present three modifications to the MT training data to improve the accuracy of a state-of-theart syntax MT system: re-structuring changes the syntactic structure of training parse trees to enable reuse of...
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Syntax-based approaches to statistical MT require syntax-aware methods for acquiring their underlying translation models from parallel data. This acquisition process can be driven by syntactic trees for either the source or target language, or by trees on both sides. Work to date has demonstrated that using trees for both sides suffers from severe coverage problems. This is primarily due to the...
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ژورنال
عنوان ژورنال: Computational Linguistics
سال: 2010
ISSN: 0891-2017,1530-9312
DOI: 10.1162/coli.2010.36.2.09054