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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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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 26 February 2021

Statistical Machine Translation through Global Lexical Selection and Sentence Reconstruction

Statistical Machine Translation through Global Lexical Selection and Sentence Reconstruction

Chapter:
(p.185) 10 Statistical Machine Translation through Global Lexical Selection and Sentence Reconstruction
Source:
Learning Machine Translation
Author(s):

Srinivas Bangalore

Stephan Kanthak

Patrick Haffner

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262072977.003.0010

This chapter presents the details of training a global lexical selection model using classification techniques and sentence reconstruction models using permutation automata. It also provides a stochastic finite-state transducer (SFST) as an example of an approach that relies on local associations, and uses it to compare and contrast the approach. The chapter is organized as follows. Section 10.2 describes in detail the different stages used to train an SFST translation model, and discusses the steps in decoding a source input using the trained SFST model. Section 10.3 presents the global lexical selection and the sentence reconstruction models. Section 10.4 discusses the rationale for choosing the classifier to train the global lexical selection model. Section 10.5 reports the results of the two translation models on a few data sets and contrasts the strengths and limitations of the two approaches.

Keywords:   training, decoding, lexical selection model, stochastic finite-state transducer, machine learning

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