Improved reordering for phrase-based translation using sparse features

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Proceedings titleProceedings of the NAACL
ConferenceNAACL, June 9-14, 2013, Atlanta, Georgia
Pages2232; # of pages: 11
AbstractThere have been many recent investigations into methods to tune SMT systems using large numbers of sparse features. However, there have not been nearly so many examples of helpful sparse features, especially for phrasebased systems. We use sparse features to address reordering, which is often considered a weak point of phrase-based translation. Using a hierarchical reordering model as our baseline, we show that simple features coupling phrase orientation to frequent words or wordclusters can improve translation quality, with boosts of up to 1.2 BLEU points in Chinese-English and 1.8 in Arabic-English. We compare this solution to a more traditional maximum entropy approach, where a probability model with similar features is trained on wordaligned bitext. We show that sparse decoder features outperform maximum entropy handily, indicating that there are major advantages to optimizing reordering features directly for BLEU with the decoder in the loop.
Publication date
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number21270979
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Record identifierbc772d89-6cbc-4c0e-9a8d-06fe29caf286
Record created2014-02-20
Record modified2016-05-09
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