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Bayesian Word Alignment for Statistical Machine Translation Authors: Coskun Mermer, Murat Saraclar Present by Jun Lang 2011-10-13 I2R SMT-Reading Group
Paper info ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Core Idea ,[object Object],[object Object],[object Object],[object Object],[object Object]
Mathematics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
IBM Model 1 T as a random variable
Dirichlet Distribution ,[object Object],[object Object],[object Object],[object Object]
Dirichlet Distribution Each source word type te is a distribution over  the target vocabulary, to be a Dirichlet distribution Avoid rare words acting  as “garbage collectors”
Dirichlet Distribution sample   the  unknowns  A and T in turn ¬j denotes the exclusion of the current value of aj .
Algorithm A can be arbitrary, but normal EM output is better
Results
 
 
Code View bayesalign.pl
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
3

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Bayesian word alignment for statistical machine translation