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Variational Decoding for Statistical Machine Translation Zhifei Li and Jason Eisner and Sanjeev Khudanpur
 

Summary: Variational Decoding for Statistical Machine Translation
Zhifei Li and Jason Eisner and Sanjeev Khudanpur
Department of Computer Science and Center for Language and Speech Processing
Johns Hopkins University, Baltimore, MD 21218, USA
zhifei.work@gmail.com, jason@cs.jhu.edu, khudanpur@jhu.edu
Abstract
Statistical models in machine translation
exhibit spurious ambiguity. That is, the
probability of an output string is split
among many distinct derivations (e.g.,
trees or segmentations). In principle, the
goodness of a string is measured by the
total probability of its many derivations.
However, finding the best string (e.g., dur-
ing decoding) is then computationally in-
tractable. Therefore, most systems use
a simple Viterbi approximation that mea-
sures the goodness of a string using only
its most probable derivation. Instead,
we develop a variational approximation,

  

Source: Amir, Yair - Department of Computer Science, Johns Hopkins University

 

Collections: Computer Technologies and Information Sciences