Mixture-Model Adaptation for SMT

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ConferenceACL Workshop on Statistical Machine Translation, 39256, Prague, Czech Republic
AbstractWe describe a mixture-model approach to adapting a Statistical Machine Translation system for new domains, using weights that depend on text distances to mixture components. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and translation model adaptation; different methods of assigning weights; and granularity of the source unit being adapted to. The best methods achieve gains of approximately one BLEU percentage point over a state-of-the art non-adapted baseline system.
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AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number49351
NPARC number8914141
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Record identifier22ebfcf1-86e6-4af8-a8c3-ea3d51ec5b75
Record created2009-04-22
Record modified2016-05-09
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