Statistical machine translation : textbook

447 Pages·2010·1.603 MB·English

by  Philipp Koehn

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Statistical Machine Translation
Thefieldofmachinetranslationhasrecentlybeenenergizedbytheemer-
genceofstatisticaltechniques,whichhavebroughtthedreamofautomatic
language translation closer to reality. This class-tested textbook, authored
byanactiveresearcherinthefield,providesagentleandaccessibleintro-
duction to the latest methods and enables the reader to build machine
translationsystemsforanylanguagepair.
It provides the necessary grounding in linguistics and probabilities,
andcoversthemajormodelsformachinetranslation:word-based,phrase-
based,andtree-based,aswellasmachinetranslationevaluation,language
modeling, discriminative training and advanced methods to integrate lin-
guisticannotation.Thebookreportsonthelatestresearchandoutstanding
challenges,andenablesnovicesaswellasexperiencedresearcherstomake
contributionstothefield.Itisidealforstudentsatundergraduateandgrad-
uatelevel,orforanyreaderinterestedinthelatestdevelopmentsinmachine
translation.
PHILIPP KOEHN is a lecturer in the School of Informatics at the Uni-
versity of Edinburgh. He is the scientific coordinator of the European
EuroMatrixprojectandisalsoinvolvedinresearchfundedbyDARPAin
the USA. He has also collaborated with leading companies in the field,
suchasSystranandAsiaOnline.Heimplementedthewidelyuseddecoder
Pharaoh, and is leading the development of the open source machine
translationtoolkitMoses.

Statistical Machine Translation
Philipp Koehn
SchoolofInformatics
UniversityofEdinburgh
CAMBRIDGEUNIVERSITYPRESS
Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore,
São Paulo, Delhi, Dubai, Tokyo
Cambridge University Press
The Edinburgh Building, Cambridge CB2 8RU, UK
Published in the United States of America by Cambridge University Press, New York
www.cambridge.org
Information on this title: www.cambridge.org/9780521874151
© P. Koehn 2010
This publication is in copyright. Subject to statutory exception and to the
provision of relevant collective licensing agreements, no reproduction of any part
may take place without the written permission of Cambridge University Press.
First published in print format 2009
ISBN-13 978-0-511-69132-4 eBook (NetLibrary)
ISBN-13 978-0-521-87415-1 Hardback
Cambridge University Press has no responsibility for the persistence or accuracy
of urls for external or third-party internet websites referred to in this publication,
and does not guarantee that any content on such websites is, or will remain,
accurate or appropriate.
For Trishann and Phianna

Contents
Preface pagexi
I Foundations 1
1 Introduction 3
1.1 Overview 4
1.2 HistoryofMachineTranslation 14
1.3 Applications 20
1.4 AvailableResources 23
1.5 Summary 26
2 Words,Sentences,Corpora 33
2.1 Words 33
2.2 Sentences 45
2.3 Corpora 53
2.4 Summary 57
3 ProbabilityTheory 63
3.1 EstimatingProbabilityDistributions 63
3.2 CalculatingProbabilityDistributions 67
3.3 PropertiesofProbabilityDistributions 71
3.4 Summary 75
II Core Methods 79
4 Word-BasedModels 81
4.1 MachineTranslationbyTranslatingWords 81
4.2 LearningLexicalTranslationModels 87
4.3 EnsuringFluentOutput 94
4.4 HigherIBMModels 96
4.5 WordAlignment 113
4.6 Summary 118
vii
viii Contents
5 Phrase-BasedModels 127
5.1 StandardModel 127
5.2 LearningaPhraseTranslationTable 130
5.3 ExtensionstotheTranslationModel 136
5.4 ExtensionstotheReorderingModel 142
5.5 EMTrainingofPhrase-BasedModels 145
5.6 Summary 148
6 Decoding 155
6.1 TranslationProcess 156
6.2 BeamSearch 158
6.3 FutureCostEstimation 167
6.4 OtherDecodingAlgorithms 172
6.5 Summary 176
7 LanguageModels 181
7.1 N-GramLanguageModels 182
7.2 CountSmoothing 188
7.3 InterpolationandBack-off 196
7.4 ManagingtheSizeoftheModel 204
7.5 Summary 212
8 Evaluation 217
8.1 ManualEvaluation 218
8.2 AutomaticEvaluation 222
8.3 HypothesisTesting 232
8.4 Task-OrientedEvaluation 237
8.5 Summary 240
III Advanced Topics 247
9 DiscriminativeTraining 249
9.1 FindingCandidateTranslations 250
9.2 PrinciplesofDiscriminativeMethods 255
9.3 ParameterTuning 263
9.4 Large-ScaleDiscriminativeTraining 272
9.5 PosteriorMethodsandSystemCombination 278
9.6 Summary 283
10 IntegratingLinguisticInformation 289
10.1Transliteration 291
10.2Morphology 296
10.3SyntacticRestructuring 302