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» Forest-to-String Statistical Translation Rules
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MT
2010
134views more  MT 2010»
13 years 5 months ago
Improve syntax-based translation using deep syntactic structures
This paper introduces deep syntactic structures to syntax-based Statistical Machine Translation (SMT). We use a Head-driven Phrase Structure Grammar (HPSG) parser to obtain the de...
Xianchao Wu, Takuya Matsuzaki, Jun-ichi Tsujii
JMLR
2011
187views more  JMLR 2011»
13 years 2 months ago
Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation
We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to learn the grammatical rules and context dependent changes using ...
Yizhao Ni, Craig Saunders, Sándor Szedm&aac...
ACL
2010
13 years 5 months ago
Constituency to Dependency Translation with Forests
Tree-to-string systems (and their forestbased extensions) have gained steady popularity thanks to their simplicity and efficiency, but there is a major limitation: they are unable...
Haitao Mi, Qun Liu
EMNLP
2004
13 years 8 months ago
Error Measures and Bayes Decision Rules Revisited with Applications to POS Tagging
Starting from first principles, we re-visit the statistical approach and study two forms of the Bayes decision rule: the common rule for minimizing the number of string errors and...
Hermann Ney, Maja Popovic, David Sündermann
EMNLP
2007
13 years 8 months ago
Syntactic Re-Alignment Models for Machine Translation
We present a method for improving word alignment for statistical syntax-based machine translation that employs a syntactically informed alignment model closer to the translation m...
Jonathan May, Kevin Knight