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Learning Machine Translation$
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Cyril Goutte, Nicola Cancedda, Marc Dymetman, and George Foster

Print publication date: 2008

Print ISBN-13: 9780262072977

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262072977.001.0001

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Toward Purely Discriminative Training for Tree-Structured Translation Models

Toward Purely Discriminative Training for Tree-Structured Translation Models

(p.131) 7 Toward Purely Discriminative Training for Tree-Structured Translation Models
Learning Machine Translation

Benjamin Wellington

Joseph Turian

I. Dan Melamed

The MIT Press

This chapter presents a method for training all the parameters of a syntax-aware statistical machine translation (MT) system in a discriminative manner. The system outperforms a generative syntax-aware baseline. Although all the standard information sources necessary for a state-of-the-art MT system have yet to be added, but the scalability of the system suggests that the main obstacle to doing so has been overcome. The next step is to generalize the tree transducer into a bitree transducer, so that it can modify the target side of the bitree after it is inferred from the source side.

Keywords:   training, statistical machine translation, bitree transducer

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