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First-and Second-Order Expectation Semirings with Applications to Minimum-Risk Training on Translation Forests
 

Summary: First- and Second-Order Expectation Semirings
with Applications to Minimum-Risk Training on Translation Forests
Zhifei Li and Jason Eisner
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
Abstract
Many statistical translation models can be
regarded as weighted logical deduction.
Under this paradigm, we use weights from
the expectation semiring (Eisner, 2002), to
compute first-order statistics (e.g., the ex-
pected hypothesis length or feature counts)
over packed forests of translations (lat-
tices or hypergraphs). We then introduce
a novel second-order expectation semir-
ing, which computes second-order statis-
tics (e.g., the variance of the hypothe-
sis length or the gradient of entropy).
This second-order semiring is essential for

  

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

 

Collections: Computer Technologies and Information Sciences