Table Of ContentInterrogating the concepts of allegiance and identity in a globalised world L
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involves renewing our understanding of membership and participation n
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within and beyond the nation-state. Allegiance can be used to define a b Advances
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singular national identity and common connection to a nation-state. In a
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global context, however, we need more dynamic conceptions to understand o in Empirical
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the importance of maintaining diversity and building allegiance with
others outside borders. Understanding how allegiance and identity are
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being reconfigured today provides valuable insights into important
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contemporary debates around citizenship.
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“This book reveals how public and international law understand allegiance
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Resources and Technologies
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Cover image: unknown artist’s photograph of a
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Advances in Empirical Translation Studies
Empiricaltranslationstudiesisarapidlyevolvingresearcharea.Thisvolume,
writtenbyworld-leadingresearchers,demonstratestheintegrationoftwonew
researchparadigms:sociallyorientedanddata-drivenapproachestoempirical
translationstudies.Thesetwomodelsexpandcurrenttranslationstudiesand
stimulate reader debates around how development of quantitative research
methods and integration with advances in translation technologies would
significantly increase the research capacities of translation studies. Highly
engaging,thevolumepioneersthedevelopmentofsociallyorientedinnova-
tiveresearchmethodstoenhancethecurrentresearchcapacitiesoftheoretical
(descriptive) translation studies in order to tackle real-life research issues,
such as environmental protection and multicultural health promotion.
Illustrative case studies are used, bringing insight into advanced research
methodologies of designing, developing and analysing large-scale digital
databasesformultilingualand/ortranslationresearch.
meng ji is Professor of Translation Studies and Chinese Studies at the
University of Sydney, Australia. She has published extensively on corpus
translation studies, contrastive linguistics and quantitative translation
methodologies.
michael oakes is a reader in Computational Linguistics in the Research
Institute in Information and Language Processing at the University of
Wolverhampton,UK.Hisresearchinterestsarecorpuslinguistics,informa-
tionretrievalandstudiesofdisputedauthorship.
Advances in Empirical Translation
Studies
Developing Translation Resources and
Technologies
Editedby
Meng Ji
UniversityofSydney
Michael Oakes
UniversityofWolverhampton
UniversityPrintingHouse,CambridgeCB28BS,UnitedKingdom
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CambridgeUniversityPressispartoftheUniversityofCambridge.
ItfurtherstheUniversity’smissionbydisseminatingknowledgeinthepursuitof
education,learning,andresearchatthehighestinternationallevelsofexcellence.
www.cambridge.org
Informationonthistitle:www.cambridge.org/9781108423274
DOI:10.1017/9781108525695
©CambridgeUniversityPress2019
Thispublicationisincopyright.Subjecttostatutoryexception
andtotheprovisionsofrelevantcollectivelicensingagreements,
noreproductionofanypartmaytakeplacewithoutthewritten
permissionofCambridgeUniversityPress.
Firstpublished2019
PrintedandboundinGreatBritainbyClaysLtd,ElcografS.p.A.
AcataloguerecordforthispublicationisavailablefromtheBritishLibrary.
LibraryofCongressCataloging-in-PublicationData
Names:Ji,Meng,1982–editor.|Oakes,MichaelP.,editor.
Title:Advancesinempiricaltranslationstudies:developingtranslationresources
andtechnologies/editedbyMengJi,MichaelOakes.
Description:NewYork,NY:CambridgeUniversityPress,2019.|Includes
bibliographicalreferencesandindex.
Identifiers:LCCN2019008010|ISBN9781108423274(hardback)
Subjects:LCSH:Translatingandinterpreting–Research–Methodology.|
Machinetranslating–Research–Methodology.|Computationallinguistics.|
BISAC:LANGUAGEARTS&DISCIPLINES/Linguistics/General.
Classification:LCCP306.5.A2782019|DDC418/.02–dc23
LCrecordavailableathttps://lccn.loc.gov/2019008010
ISBN978-1-108-42327-4Hardback
CambridgeUniversityPresshasnoresponsibilityforthepersistenceoraccuracyof
URLsforexternalorthird-partyinternetwebsitesreferredtointhispublication
anddoesnotguaranteethatanycontentonsuchwebsitesis,orwillremain,
accurateorappropriate.
Contents
ListofFigures pagevii
ListofTables ix
ListofContributors xi
Preface xiii
meng ji and michael oakes
1 AdvancesinEmpiricalTranslationStudies 1
meng ji
2 DevelopmentofEmpiricalMultilingualAnalyticalInstruments 13
meng ji
3 StatisticsforCorpus-BasedandCorpus-DrivenApproachesto
EmpiricalTranslationStudies 28
michael oakes
4 TheEvolvingTreatmentofSemanticsinMachineTranslation 53
mark seligman
5 TranslatingandDisseminatingWorldHealthOrganization
Drinking-Water-QualityGuidelinesinJapan 77
meng ji, glenn hook and fukumoto fumiyo
6 DevelopingMultilingualAutomaticSemanticAnnotation
Systems 94
laura lo¨fberg and paul rayson
7 LeveragingLargeCorporaforTranslationUsingSketchEngine 110
sara moze and simon krek
8 DevelopingComputerisedHealthTranslationReadability
EvaluationTools 145
meng ji and zhaoming gao
v
vi Contents
9 ReorderingTechniquesinJapaneseandEnglishMachine
Translation 164
masaaki nagata
10 AudiovisualTranslationinMercurialMediascapes 177
jorge d´ıaz-cintas
11 ExploitingData-DrivenHybridApproachestoTranslationinthe
EXPERTProject 198
constantin ora˘san, carla parra escart´ın, lianet
sepu´lveda torres and eduard barbu
12 AdvancesinSpeech-to-SpeechTranslationTechnologies 217
mark seligman and alex waibel
13 ChallengesandOpportunitiesofEmpiricalTranslationStudies 252
meng ji and michael oakes
Index 265
Figures
2.1 Introducingmulti-sectoralinteractiontoadvance
environmentalhealthrisk(EHR)transition page15
3.1 Regressionlineshowingtherelationbetweenthepercentage
frequenciesofthewords‘that’and‘of’inEnglishtextswritten
bynon-nativespeakers 34
3.2 Confidencelimitsfortheuseof‘that’inEnglishtextsbynative
speakersofvariouslanguagepairs 37
3.3 Boxplotshowinghowthenumberoftypesper1,000words
dependsontheinteractionbetweenmodeandlanguage 39
3.4 PCAfornative-speaker-authored(n),non-native-speaker-
authored(u)translated(t)textsintheENNTTcorpus 44
3.5 PCAfornative-speaker-authored(n)andnon-native-speaker-
authored(u)textsintheENNTTcorpus 45
3.6 PCAfortextstranslatedintoEnglishfromGermanic-and
Romance-language-familyoriginals 46
3.7 PCAwheretranslatedtextsaregroupedaccordingtotheir
originallanguages 49
4.1 Contrastingsyntacticandsemanticintermediatestructures 55
4.2 TheVauquoisTriangle 56
4.3 AhybridintermediatestructurefromtheASURAsystem 60
4.4 AsentencerepresentationintheUNLinterlingua 61
4.5 SentencerepresentationsintheIFinterlingua 62
4.6 Partofaphrasetableforstatisticalmachinetranslation 63
4.7 TwovectorspacesforEnglish,withcorrespondingSpanish
spaces 64
4.8 Connectionsamongrulesforminganetwork 66
4.9 Aneuralnetworkshowingencoding,decoding,andattention 67
4.10 AfragmentofGoogle’sKnowledgeGraph 71
5.1 PathanalysisofthetranslationanddisseminationofWHO
GuidelinesinJapan 83
7.1 BuildingaqueryfortheidiomgraspthenettlewiththeCQL
builder 113
vii
viii ListofFigures
7.2 ParallelqueryusingtheSloveneandEnglishEur-Lex
judgementssubcorpora 114
7.3 Slovene–Englishparallelconcordancefor‘postopek
soodločanja’inthe‘Sentenceview’ 115
7.4 Generatingamulti-levelfrequencylistinSketchEngine 118
7.5 Frequencylistfeaturingcandidatetranslationswith
‘framework’ 118
7.6 Theword-sketchfunctionality 120
7.7 Wordsketchfor‘effective’ 121
7.8 Wordsketchfor‘efficient’ 122
7.9 Sketchdifferencecomparingtheadjectives‘effective’and
‘efficient’ 125
7.10 ExtractfromtheEur-Lex-generatedsketchdifference
comparing‘security’and‘safety’ 126
7.11 Multi-wordsketchfor‘legalframework’ 128
7.12 TheSketchEnginethesaurusinaction 130
7.13 Slovene–Englishbilingualwordsketchfor‘učinkovit’and
‘effective’ 132
7.14 ShortexcerptfromtheItalian–Englishbilingualwordsketch
for‘regolamento’and‘regulation’ 134
7.15 The‘Changeextractionoptions’boxinSketchEngine 137
7.16 Englishtermcandidatesextractedfromthespecialisedparallel
corpus 138
7.17 Slovenetermcandidatesextractedfromthespecialisedparallel
corpus 139
7.18 DefiningbasicparametersforthenewcorpusinSketchEngine 140
7.19 Step1ofthecorpus-buildingprocedure 140
7.20 Corpusfilesandthe‘Compilecorpus’command 140
7.21 Single-andmulti-wordSlovenetermsextractedfromthe
LisbonTreatytext 142
9.1 Outlineofstatisticalmachinetranslation 165
9.2 Pre-orderingmethodbasedonheadfinalizationappliedtoan
Englishbinaryconstituenttree 166
9.3 Pre-orderingmethodbasedonheadfinalizationappliedto
EnglishandChinesedependencystructures 167
9.4 Pre-orderingmethod-basedmaximizationofrankcorrelation
coefficientappliedtoJapanesebinaryconstituenttree 170
9.5 Pre-orderingmethod-basedmaximizationofrankcorrelation
coefficientappliedtoword-basedtypeddependencyfor
Japanese 171
9.6 CorrelationbetweenhumanevaluationandBLEU 173
11.1 MaincomponentsofActivaTM 206
Tables
2.1 WordfrequenciesofoccurrencebycategoryinFACTIVA
(Australia) page21
2.2 Cross-sectoralcorrelationscores:Australia 23
2.3 ComparisonbetweenEPIandMulti-Sectoral
Interaction(MSI) 24
2.4 Spearman’scorrelationtest 25
5.1 Regressionweights(targetsandgoals) 84
5.2 Regressionweights(standardsandprinciples) 85
5.3 Regressionweights(approachesandmethods) 86
5.4 Regressionweights(actions) 86
5.5 Regressionweightsbetweenthemediaandindustrialsectors 88
5.6 Spearman’scorrelationtestofeffectsfromdirectsourcesof
informationandthemassmedia 90
6.1 USASsemantictaxonomy 96
8.1 Exploratoryfactoranalysismodel 150
8.2 Rotatedcomponentmatrixofthetwo-dimensionalfactor
analysismodel 151
8.3 Modelfit:Discriminantanalysis 154
8.4 Discriminantanalysis(stepwise) 155
8.5 Standardisedcanonicaldiscriminantfunctioncoefficients 156
8.6 Structurematrixofthefive-itemmodel 156
8.7 Classificationresult 156
8.8 Modelfit 158
8.9 Wilks’lambda 158
8.10 Classificationresults 159
9.1 Wordsegmentation,part-of-speechtagginganddependency
parsingaccuraciesforpatentsentences 169
9.2 TranslationaccuracyinBLEUforbaseline(phrase-based
SMT)andheadfinalization(phrase-basedSMTwith
pre-ordering) 169
ix
