Discriminative Instance Weighting for Domain Adaptation in Statistical Machine Translation

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Proceedings titleProceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
ConferenceEmpirical Methods in Natural Language Processing, October 9-11, 2010, Cambridge, Massachusetts, USA
Pages451459; # of pages: 9
AbstractWe describe a new approach to SMT adaptation that weights out-of-domain phrase pairs according to their relevance to the target domain, determined by both how similar to it they appear to be, and whether they belong to general language or not. This extends previous work on discriminative weighting by using a finer granularity, focusing on the properties of instances rather than corpus components, and using a simpler training procedure. We incorporate instance weighting into a mixture-model framework, and find that it yields consistent improvements over a wide range of baselines.
Publication date
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number16350483
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Record identifier00f9f87a-ba75-4895-9e8f-cdf027ffd599
Record created2010-11-10
Record modified2016-05-09
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