Controlled Document Authoring in a Machine Translation Age

236 Pages·2020·12.353 MB·English

by  Rei Miyata

Routledge Studies in Translation Technology
CONTROLLED DOCUMENT
AUTHORING IN A MACHINE
TRANSLATION AGE
Rei Miyata
Controlled Document Authoring
in a Machine Translation Age
This book explains the concept, framework, implementation, and evaluation of
controlled document authoring in this age of translation technologies. Machine
translation(MT)isroutinelyusedinmanysituations,bycompanies,governments,
andindividuals.Despiterecentadvances,MTtoolsarestillknowntobeimperfect,
sometimes producing critical errors. To enhance the performance of MT,
researchers and language practitioners have developed controlled languages that
imposerestrictionsontheformorlengthofthesource-languagetext.However,a
fundamental, persisting problem is that both current MT systems and controlled
languages deal only with the sentence as the unit of processing. To be effective,
controlledlanguagesmustbecontextualisedatthedocumentlevel,consequently
enabling MT to generate outputs appropriate for their functional context within
thetargetdocument.WithaspecificfocusonJapanesemunicipaldocuments,this
book establishes a framework for controlled document authoring by integrating
various research strands including document formalisation, controlled language,
andterminologymanagement.Itthenpresentsthedevelopmentandevaluationof
anauthoringsupportsystem,MuTUAL,thatisdesignedtohelpnon-professional
writers create well-organised documents that are both readable and translatable.
The book provides useful insights for researchers and practitioners interested
in translation technology, technical writing, and natural language processing
applications.
ReiMiyata,PhD,isanAssistantProfessorattheGraduateSchoolofEngineering,
NagoyaUniversity,Japan.
RoutledgeStudiesinTranslationTechnology
SeriesEditor:ChanSin-wai
This cutting-edge research series examines translation technology and
explores the relationships between human beings and machines in
translating the written and spoken word. The series welcomes authored
monographsandeditedcollections.
TheFutureofTranslationTechnology
TowardsaWorldwithoutBabel
ChanSin-wai
TheHumanFactorinMachineTranslation
EditedbyChanSin-wai
ControlledDocumentAuthoringinaMachineTranslationAge
ReiMiyata
Formoreinformationonthisseries,pleasevisit
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Controlled Document
Authoring in a Machine
Translation Age
Rei Miyata
Firstpublished2021
byRoutledge
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BritishLibraryCataloguing-in-PublicationData
AcataloguerecordforthisbookisavailablefromtheBritishLibrary
LibraryofCongressCataloging-in-PublicationData
Names:Miyata,Rei,author.
Title:Controlleddocumentauthoringinamachine
translationage/ReiMiyata.
Description:London;NewYork:Routledge,2020.|
Series:Routledgestudiesintranslationtechnology|
Includesbibliographicalreferencesandindex.
Identifiers:LCCN2020020455
Subjects:LCSH:Machinetranslating.|Computationallinguistics.|
Naturallanguageprocessing(Computerscience)
Classification:LCCP308.M592020|DDC418/.020285–dc23
LCrecordavailableathttps://lccn.loc.gov/2020020455
ISBN:978-0-367-50019-1(hbk)
ISBN:978-1-003-04852-7(ebk)
TypesetinTimesNewRoman
byNewgenPublishingUK
Contents
Listoffigures vii
Listoftables viii
Listofabbreviations x
Preface xi
Acknowledgements xiii
PARTI
Researchbackground 1
1 Introduction 3
2 Relatedwork 12
PARTII
Controlleddocumentauthoring 57
3 Documentformalisation 59
4 Controlledlanguage 68
5 CLcontextualisation 91
6 Terminologymanagement 101
PARTIII
MuTUAL:Anauthoringsupportsystem 125
7 Systemdevelopment 127
8 EvaluationofCLviolationdetectioncomponent 138
9 Systemusabilityevaluation 144
vi Contents
PARTIV
Conclusion 167
10 Researchfindingsandoutlook 169
Appendices 177
Bibliography 199
Index 215
Figures
1.1 Personalsealregistrationprocedure(excerptedfromthewebsite
ofShinjukuCity) 5
1.2 Chapterorganisationwithresearchquestions 10
2.1 Functionalelementsofresearchpapers(Kando,1997,p.3) 18
2.2 Machinetranslationworkflowwithhumanintervention 23
3.1 Functionalelementsofmunicipalproceduraldocuments 62
3.2 Exampleofthefunctionalelementsintheprocedureforpersonal
sealregistration(excerptedfromadocumentinCLAIR) 63
3.3 Exampleofthefunctionalelementsintheprocedureforpersonal
sealregistration(excerptedfromadocumentinHamamatsu) 63
3.4 AnalysisofanexistingmunicipaldocumentusingourDITA
framework 65
3.5 Modelexampleofthesealregistrationprocedure 66
4.1 MTqualityquestionnaire–Step2(when[1]or[2]isselectedin
Step1) 74
4.2 Source-readabilityquestionnaire 83
5.1 ApproachestoresolveincompatibilitiesbetweenCL andCL
st tt
forSteps(*undesirablesentence) 93
5.2 Revisedflowofpre-andpost-translationprocessingforSteps
(*undesirablesentence) 99
6.1 Termregistrationplatform 105
6.2 Thefrequencyspectrumoftermsinmunicipalcorpus(m:frequency
class;V(m,N):numberoftypeswithfrequencym) 106
6.3 Growthcurveofterminologiesinmunicipalcorpus 119
7.1 ModulesofMuTUAL 128
7.2 Tasktopictemplate 130
7.3 CLauthoringassistant,MTandbacktranslation(BT) 131
7.4 Terminologycheckfunction 132
7.5 CLruleselectionmodal 133
7.6 CLguidelinemodal 134
7.7 Similartextsearch 136
9.1 Userinterface 147
Tables
2.1 SeveninformationtypesdefinedinInformationMapping
(Horn,1989,pp.110–111) 15
2.2 Controlledwritingprocessesandsupportmechanisms 50
3.1 Examplesofhierarchicallevelsofwebsites 60
3.2 Thenumberofdocumentsineachcategoryofmunicipal-life
information(thenumberofproceduraldocumentsisinparenthesis) 61
3.3 SpecialisationoftheDITATasktopictomunicipalprocedures 64
4.1 AlistoftechnicalwritingbasedCLrules(CL-T) 70
4.2 Exampleofsource-textrewriting 71
4.3 AlistofrewritingtrialbasedCLrules(CL-R) 72
4.4 Resultcategories 75
4.5 OverallresultsofMTquality(CL-T) 76
4.6 OverallresultsofMTquality(CL-R) 76
4.7 Improvementin[MT–Useful]category(CL-T) 78
4.8 Improvementin[MT–Useful]category(CL-R) 78
4.9 Overallresultsafteroptimalruleswereselected(CL-T) 81
4.10 Overallresultsafteroptimalruleswereselected(CL-R) 82
4.11 ImprovementinJapanesereadability(CL-T) 83
4.12 ImprovementinJapanesereadability(CL-R) 84
4.13 SelectedoptimalrulesfortwoMTsystems(commonrulesare
showninbold) 87
5.1 Linguisticspecification 92
5.2 ThenumberofMToutputsthatrealisethedesiredlinguistic
formsintheEnglishtranslationbeforeandafterpre-translation
processing 95
5.3 ExampleMToutputsbeforeandafterapplyingRule1(inserted
segmentinbrackets) 96
5.4 ExampleofremainingdisconformitytoCL 96
tt
5.5 ExampleMToutputsbeforeandafterapplyingRule2‘shiro’
(transformedsegmentinbrackets) 97
5.6 ExampleMToutputsbeforeandafterapplyingRule2‘shinasai’
(transformedsegmentinbrackets) 98
Tables ix
5.7 ExampleMToutputsbeforeandafterapplyingRule2
‘shitekudasai’(transformedsegmentinbrackets) 98
6.1 Basicstatisticsofextractedsentences 103
6.2 Thenumberoftermsextractedandtheiroccurrencesinthecorpus 106
6.3 The20mostfrequenttermsinthecorpus(beforecontrolling) 107
6.4 Exampleofextractedbilingualtermpairs 108
6.5 Criteriafordefiningpreferredandproscribedterms 112
6.6 Examinationoftermvariations 113
6.7 Thebasicstatisticsofcontrolledterminology 113
6.8 The20mostfrequentcontrolledtermsoccurredinthecorpus 114
6.9 PopulationtypesE[S]andcoverageCR 118
6.10 Growthrate 120
6.11 Shiftinthecoverageratio(%) 120
8.1 Resultsofthebenchmarkevaluation 140
9.1 Quantitativeaspectstobemeasured 145
9.2 CLrulesandimplementation(withconfidencescores) 146
9.3 After-scenarioquestionnaire(ASQ) 148
9.4 Systemusabilityscale(SUS) 149
9.5 Effectivenessforeachcondition 151
9.6 Correctionrateforeachrule(*implementedrule) 152
9.7 ResultofMTqualityevaluation(systemB) 153
9.8 ResultofMTqualityevaluation(systemD) 153
9.9 ExampleofSTandMT(systemD)ofdifferentconditions 154
9.10 ResultofSTqualityevaluation 155
9.11 Timeefficiency 155
9.12 Detailededitlog 156
9.13 Editdistance 157
9.14 ResultofquestionnaireASQ(satisfactionwiththetask) 158
9.15 ResultofquestionnaireSUS(satisfactionwiththesystem) 159
9.16 Textsimilaritybetweenparticipants 161
9.17 Exampleofhigh-text-similaritysentence 162
9.18 Exampleoflow-text-similaritysentence 163
9.19 Tasktimetransition(timepersentence,inseconds) 164
9.20 Tasktimetransition(timepercharacter,inseconds) 164