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Using Paraphrases for Parameter Tuning in Statistical Machine Translation Nitin Madnani, Necip Fazil Ayan, Philip Resnik & Bonnie J. Dorr
 

Summary: Using Paraphrases for Parameter Tuning in Statistical Machine Translation
Nitin Madnani, Necip Fazil Ayan, Philip Resnik & Bonnie J. Dorr
Institute for Advanced Computer Studies
University of Maryland
College Park, MD, 20742
{nmadnani,nfa,resnik,bonnie}@umiacs.umd.edu
Abstract
Most state-of-the-art statistical machine
translation systems use log-linear models,
which are defined in terms of hypothesis fea-
tures and weights for those features. It is
standard to tune the feature weights in or-
der to maximize a translation quality met-
ric, using held-out test sentences and their
corresponding reference translations. How-
ever, obtaining reference translations is ex-
pensive. In this paper, we introduce a new
full-sentence paraphrase technique, based
on English-to-English decoding with an MT
system, and we demonstrate that the result-

  

Source: Ayan, Necip Fazil - Speech Technology & Research Laboratory , SRI International
Dorr, Bonnie - Institute for Advanced Computer Studies, University of Maryland at College Park

 

Collections: Computer Technologies and Information Sciences