理工类英语学术论文写作教程答案

KeytoExercises

ChapterOne IntroductiontoAcademicWriting

Task1

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Task2

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A4

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B5

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Task3

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Task4

Extract1VersionBExtract2VersionAExtract3VersionA

ChapterTwoWritingtheIntroduction

Task1

Steps

Paragraphnumbers(example1)

Paragraph numbers(example2)

Providebackgroundinformation

①

①

Summarize

previousresearches

②③④

Identify the

limitationsorgaps

⑤⑥

②③

Describe thepresentresearch

⑦⑧

④⑤⑥

Task2

Describethepresentresearch

Providebackgroundinformation

Summarizepreviousresearches+Identifythelimitationsorgaps

Describethepresentresearch

Task5

Thephenomenonofdigitalinnovationhascapturedtheattentionofscholarsandpractitionersalikeacrossmultipledisciplinessuchaseconomics,strategy,andmarketing.Theubiquityofdigitaltechnologyhasnotonlychangedthewaywestrategizeandorganizetocreateinnovation,butcarryingout“newcombinationsofdigitalandphysicalcomponentstoproducenovelproducts”haschangedthenatureofinnovationitself.Particularly,thereisaremarkableinterconnectednessbetweensocialactorsanddigitaltechnologiesinvolvedindigitalinnovations.Theassociatedemergenceofcomplexsociotechnicalsystemsrequiresresearchtoconsider“technicalartifactsaswellastheindividuals/collectivesthatdevelopandusetheartifactsinsocialcontexts”.

Ubiquity:Derivedfromtheadjective“ubiquitous”,thisnounreferstothewidespreadpresenceofdigitaltechnology.Itismoreconcisethansaying"thefactthatdigitaltechnologyiseverywhere."Interconnectedness: A noun formed from the adjective“interconnected,”itreferstothestateofbeinginterconnected.Itismoreconciseandabstractthansaying“thestateinwhichthingsareconnected.”

Emergence:Derivedfromtheverb“toemerge,”thisnounreferstotheactofcomingintovieworexistence.Itisconciseandallowsforamoreabstractdiscussionoftheprocess.

Task6

1)1.Forexample 2.Although/While

3.however 4.thus

1.however, 2.Thus

3.Although/While 4.Forexample

1.Since 2.Although

3.Therefore

1.Despite 2.sothat

3.Forinstance 4.Thus

However

Task7

④Themassive-scalepre-trainedlanguagemodels(PLMs)(Hanetal.,2021a)havebeenproventobebackbonesforsolvingavarietyofNLPtasks(Kowsarietal.,2019;Rajpurkaretal.,2016).TofurtheradaptthesePLMstodownstreamtaskssuchasclassification,traditionalapproachesfinetunethelanguagemodelsthroughanextraclassifier(HowardandRuder,2018).However,whentask-specificdataislimited(Braggetal.,2021),trainingtheextraclassifiereffectivelyischallengingduetothegapbetweenpre-trainingtasks(e.g.,maskedlanguagemodeling)andfine-tuningtasks(e.g.,classificationandregression).ThisgapimpedesthefastadaptationofPLMstodownstreamtasks.

③Recently,prompt-basedtuning(SchickandSchütze,2021;Liuetal.,2021)hasrisentobeapowerfulwayforfew-shotlearningbybridgingthegapbetweenthepre-trainingstageanddownstreamtaskstage.Inprompt-basedtuning,theinputtextsarewrappedwithtask-specifictemplatestore-formalizetheoriginaltaskasacloze-styletask.Forexample,intopicclassificationtask,wecanusetemplate“Thistopicisabout[MASK]”,whereistheplaceholderforinputsentences.ThePLMsareaskedtoinferthewordstofillin[MASK]andthewordsarefurthermappedtocorrespondinglabelsthroughaverbalizer(e.g.“sports”forlabel“Sports”).Verbalizersareofgreatimportanceinprompt-basedtuning(Gaoetal.,2021)sincetheyarethebridgesbetweenmodeloutputsandthefinalpredictions.Howtobuildeffectiveverbalizersforprompt-basedtuning—

especiallyformany-classclassification,isacriticalissueinprompt-basedtuning.

⑥Typically,mostcurrentworksadoptthreekindsofverbalizers:manualverbalizers,search-basedverbalizers,andsoftverbalizers.WeshowthembyanexampleinFigure1.Human-designedmanualverbalizerspicksomelabelwords(e.g.labelnames)todepictclasses.Theseverbalizersarepowerfulacrossmultipletasks(SchickandSchütze,2021).Despitetheirsuccess,amajordrawbackrootsinthestrongassumptionthatweownpreciseunderstandingsofdownstreamtasksandareabletosumupeachclasswithseveralwords.Withouttask-specificpriorknowledge,selectingappropriatelabelwordsisnon-trivial.Further,theyalsoneedintensivehumanlaborswhenfacingmanyclasses.Tomitigatetheseissues,search-basedverbalizersaimatfindingsuitablelabelwordsfromvocabularywithalgorithms(Schicketal.,2020;Shinetal.,2020;Gaoetal.,2021)andsoftverbalizersusetrainabletokenswhichareoptimizedduringtuning(Hambardzumyanetal.,2021;Zhangetal.,2021).However,itischallengingtosearchoroptimizeadequatelyinalargevocabularyorembeddingspaceunderalow-dataregime,makingautomaticverbalizerssuboptimalcomparedwithmanualones.

①Intuitively,classproxiesinverbalizersshouldencapsulate

class-levelsemanticfeatures,whichareexpressedimplicitlybyinstances.Toobtainthesesemanticrepresentativeswithfewdata,onepromisingapproachiscomputingcentralpointsofclassinstances,namelyprototypes,asapproximation.Tothisend,wemanagetoestimateprototypevectorsforeachclasstoserveasverbalizer.Summarizedfrominstances,prototypesaresupposedtoestablishconceptssimilarwithhuman-designedlabels.

⑤Inthiswork,weintroduceprototypesintothisproblemandproposeprototypicalverbalizer(ProtoVerb),whichlearnsclassprototypesfromtrainingdatatobuildverbalizersautomatically.Forprototypelearning,inspiredbytheideaofPCL(Lietal.,2021),ProtoVerbtrainstheprototypevectorsbycontrastivelearningwith

theInfoNCEestimator(Oordetal.,2018).Specifically,ouroptimizationobjectiveincludestwocomponents:Thefirstpartisaninstance-instancelosstoclusterintra-classinstancesandseparateinter-classinstances;Thesecondpartisaninstance-prototypelosswhichenforcestheprototypestobecenterpointsofclasses.Comparedwithotherverbalizerconstructionmethods,ProtoVerblearnscontinuousvectorsstraightfromtraininginstancesefficiently,whichmakesitaplug-in-and-playalgorithmwithhighflexibility.

②ToverifytheeffectivenessofProtoVerb,weconductextensiveexperimentsontopicclassificationandentitytypingtasks.WestudytwodifferentsettingswhereProtoVerbcanwork:(1)Whenmanualverbalizersareavailable,ProtoVerbcanplayasanextraverbalizerintheinferencestage.ResultsshowthatProtoVerbconsistentlyimprovestheclassificationperformancewithlowcost,andevenuntunedPLMsbenefitlargely.(2)Considerarealisticsettingwhereonlyalimitednumberofsamplesareprovidedwithnomanualverbalizers,ProtoVerbalsoproducesverbalizersofhighquality.ExperimentalresultsdemonstratethatProtoVerbsignificantlyoutperformsexistingsearch-basedandsoftverbalizers.

Task8

ExtractsA1,B1,C1,D1arefromtheabstractsectionsofresearchpapers,whileExtractsA2,B2,C2,D2arefromtheintroductionsectionsofresearchpapers.

ChapterThreeWritingtheLiteratureReview

Task1

AnalysisoftheGenericStructureofExample1LiteratureReview

OverviewoftheLiteratureReview

□ObjectiveoftheLiteratureReview

Theobjectiveofthisliteraturereviewisexplicitlystatedintheopeningparagraph.Theauthorsaimtoreviewrelatedworkon:

LongtimeseriesforecastingwithTransformersLoadforecastingusingTransformerarchitecturesGlobalversuslocalmodelingapproachesTransferlearninginloadforecasting

Thesectionalsooutlinesitsinternalorganization(e.g.,“first…next…attheend…”),clearlysignalingthestructureofthereview.

□TopicstobeDiscussed

Thereviewfocusesonthefollowingmajortopics:

Extensions and performance comparison of Transformerarchitectures

Short-termloadforecastingTransformerarchitecturesTransferlearninginenergysystems

CommentsontheOverallLiterature

Theauthorsprovidegeneralevaluativecommentsaboutthefield,suchas:

Moststudiesuselocalmodels,whilefewuseglobalmodels.

Thesemodelsarenotcomparedonacommonbenchmarkdataset,butevaluatedondifferentdatasetsoncityornationallevel.

There,usuallyonlyoneloadtimeseriesisavailable,whichonlyallowsforlocalmodels.

ReviewingPreviousResearch

CitingPreviousResearch

Thereviewincludesextensivecitationofpriorstudies.Examplesinclude:Zhouetal.(2021),Wuetal.(2021),Nieetal.(2022),Zengetal.(2022)

OrderingCitationsbyFocus

Thecitedstudiesareorganizedgroupedanddiscussedintermsofthethemesortopics.

CommentingontheStrengthsand/orWeaknessesoftheStudies

Theauthorsprovideevaluativecommentary:

PatchTST(Nieetal.2022)isaglobalTransformerwithpatchedinputsandissuperiortoLTSF-Linear(Zengetal.2022)onthesixdatasets.

IdentifyingResearchGapsintheExistingLiteratureTheliteraturereviewidentifiesgaps:

Thisworkdoesnotoptimizethemodel’slookbacksizeandthereforeachievessuboptimalresults.

ThemodelsarenotcomparedtotheTransformerarchitecturesforlongtimeseries.

CallingforMoreResearchontheTopic

Thereviewdoesnotexplicitlystatethat“moreresearchisneeded.”However,theidentifiedlimitationsandgapsimplicitlyjustifythenecessityoffurtherresearch.Thecallforresearchisthereforeimplicitratherthanexplicit.

RelatingPreviousResearchwiththePresentResearch

PresentingthePurposeofthePresentResearch

Theauthorsexplicitlyrelatepreviousresearchtotheirownstudyusingstatementssuchas:

Paralleltoourwork…

Wecomparethisapproachinourexperiments.

Incontrasttotheseworks,ourtransferlearningapproach…

ListingHypothesesand/orResearchQuestions

Theliteraturereviewdoesnotexplicitlystatehypothesesorresearchquestions.Thisisofthecaseinengineeringandcomputerscienceresearch,whereresearchquestionsareoftenembeddedinmethodologicalcomparisonsratherthanformallyarticulated.

IndicatingtheSignificanceofthePresentResearch

Thesignificanceofthepresentstudyisimpliedthroughcontrastwithpriorapproaches:

Incontrasttotheseworks,ourtransferlearningapproachistotrainageneralizedmodelonthedatafrommanyclients,withoutfine-tuningforatargettimeseries.

Task2

AccordingtoJacquesCousteau,theactivityofpeopleinAntarcticaisjeopardizingadelicatenaturalmechanismthatcontrolstheearth'sclimate.Hefearsthathumanactivitycouldinterferewiththebalancebetweenthesun,thesourceoftheearth'sheat,andtheimportantsourceofcoldfromAntarcticwatersthatflownorthandcooltheoceansandatmosphere.

Duringthetwentieslawlessnessandsocialnonconformityprevailed.Incitiesorganizedcrimeflourishedwithoutpoliceinterference,andinspiteofnationwideprohibitionofliquorsales,anyonewhowishedtobuyadrinkknewwheretogetone.MusicianslikeLouisArmstrongbecomefavorites,particularlyamongyoungpeople,asmanyturnedawayfromhighlyrespectableclassicalmusictojazz.Oneofthebestexamplesoftheanti-traditionaltrendwastheproliferationofyoung"flappers,"womenwhorebelledagainstcustombycuttingofftheirhairandshorteningtheirskirts.

Theuseofahelmetisthekeytoreducingbicyclingfatalities,whichareduetoheadinjuries75%ofthetime.Bycushioningtheheaduponimpact,ahelmetcanreduceaccidentalinjurybyasmuchas85%,savingthelivesofhundredsofvictimsannually,halfofwhomareschoolchildren.

Howmuchhigherskyscrapersofthefuturewillrisethanthepresentworldmarvel,theSearsTower,isunknown.However,thedesignofonetwiceastallisalreadyontheboards,andanarchitect,RobertSobel,thinkswecurrentlyhavesufficientknow-howtobuildaskyscraperwithover500stories.

Task3

Paraphrase2isbetter,becauseithasbetterclarity,accuracy,andconciseness.Besides,itcommunicatesthemainideamoredirectlyandavoidscloselymirroringthesentencestructureandspecificphrasesoftheoriginaltext.Incontrast,Paraphrase1islesseffectiveduetoitsslightlymorecomplexsentencestructureandlessprecisewording.Italsointroducesadditionaldetailsthatarenotexplicitlymentionedinthesourcetext(forexample,preferredbreedinggrounds).

Task5

1.proves 2.demonstrates3.aimsto4.mentioned

5.concludes6.emphasizes7.reject8.question

9.asserts 10.summarized

Task6

Problems

Suggestions

1.Lackoffocus(includeirrelevantortoobroadinformation)

Defineyourresearchquestionandscopeclearly.

Usekeywords,databases,andfilterstosearchforthemostrelevantsources.

Organizeyourliteraturereviewaroundthemes,concepts,orarguments,ratherthanjustlistingsourceschronologicallyoralphabetically.

Focusonaspecificresearchquestion,problem,ortopic,andprovideaclearand

coherentsynthesisoftherelevantliterature.

2.Poorqualityofsources(relyonpoor quality oroutdatedsources that

cannot reflect

Evaluatethequalityandrelevanceofeachsourcebeforeusingit.

Considerthefollowingcriteria:theauthor’scredentials,thepublicationdate,thesourcetype,thepeer-reviewstatus,thecitationcount,

andthebiasorperspectiveofthesource.

thecurrentstateofknowledgein

thefield)

3.Useavarietyofsources,suchasbooks,journalarticles,reports,andreviews,toavoid

relyingonasinglesourceortypeofsource.

3.Lackofcriticalanalysis(simplysummarizeor

describethesources)

Analyzeeachsourceinrelationtoyourresearchquestionandobjectives.

Compareandcontrastdifferentsourcesandperspectives.

Identifythegaps,limitations,inconsistencies,andcontroversiesintheliterature.

Explainhowyourresearchwilladdressthem.

4.Lackofstructureandcoherence

Useanoutlineoraframeworktoplanyourliteraturereview.

Followastandardformatandstyle.

Useheadings,subheadings,transitions,andsignpoststodivideandconnectyoursectionsandparagraphs.

Usecitations,references,andparaphrasingto acknowledge your sources and avoid

plagiarism.

5. Lack

synthesis and

integration ofthesources

Usesynthesistechniques,suchastables,matrices,diagrams,ormaps,toorganizeandvisualizeyoursourcesandtheirrelationships.

Usesynthesiswords,suchashowever,therefore,similarly,oralternatively,toshowtheconnectionsandcontrastsbetweenyoursources.

Useyourownvoiceandinterpretationto

drawconclusionsandimplicationsfromyourliteraturereview.

ChapterFour WritingtheMethods

Task1

Moves

Paragraphnumbers

(example1)

Paragraphnumbers

(example2)

Research

design

①

①②③

Datacollection

①

②④⑤

Materials

Experimentalprocedures

Dataanalysis

②③

④⑤

Task2

Sentences

Description

InSentence1,thewriter

explainsthemethodologyusedintheresearch.

InSentence2,thewriter

selectsfactorsfromliteraturetocreatethemodelandhypothesis.

InSentence3,thewriter

developsthemodelbasedonTPBand

extendsitwithnewconcepts.

InSentence4,thewriter

introducesanewmodelusingSEMforunderstandinguserinteractions.

InSentence5,thewriter

conductsanonlinesurveywithusersofvaryingbackgrounds.

InSentence6,thewriter

ensures robust analysis results byincludingcontrolvariables.

Task4

1.described

2.conducted

3.developed

4.provided

5.consistsof

6.collect

7.employed

8.beginswith

9.shown

10.wasusedto

Task5

1.proposed

2.discussed

3.aims

4.using

5.acquire

6.shown

7.comprises10.controlling

8.selected

9.discuss

ChapterFive

WritingtheResults

Task1

Steps

Sentencenum(example1

bers

)

Sentence

numbers(example2)

Revisit the researchquestions and/or the

methodology

①

①

Describethemethodsfor gathering or

analyzingdataindetail

0

②③④⑤⑥⑦

⑧

Locatethevisualrepresentationswherethefindingsare

presented

②

⑨

Highlightmajor

results

②③④⑤

⑩⑪

Providespecificdatatosupporttheresultsor

findings

②④⑤

⑩

Comment o

results

n the

⑥⑦

⑫

Task4

Figure4illustratestheimpactofincreasingthenumberofusersonthenumberofmessagesassociatedwiththeexchangeofkeysbetweenusersandtheserverforvariousauxiliarynoderatios.

(locateinformation)Asseen,asthenumberofusersincreases,thereisaslightchangeinthenumberofmessages,andthelowestuseofauxiliarynodesshowsthelessnumberofmessages(outliningresults).

TableIVcomparestheoverallperformancesofthePSAmodel,theVerifynetmodel,andourmodelwithvaryingusernumbersanddropoutratios.(locateinformation)Duringthesharingkeyphase,ourmodeldemonstratesalowercostthanPSAandVerifynet(outliningresults)asinourmodel,onlytheauxiliarynodeshavetosharethevaluesofαMandkMtousers,whereasintheothertwomodels,eachusermustmakesharesofbothitssecretkeyanditsprivatevalueandsharethemwitheveryotheruser.Duringthemaskinginputphase,wedidnotobserveasignificantperformancegapbetweenPSAandourmodel.VerifyNet,ontheotherhand,incursenormousoverhead,mostlybecauseofitsextensiveuseofgroupoperationstoachievebilinearpairing.Duringthephaseofunmaskinginputandaggregation,whentherearenodropouts,thecostsofPSAandourmodelarecomparable.Nevertheless,whendropoutsoccur,thecomputationcostinPSAincreasesexponentiallywhileourmodelmaintainsaconstantcomputationcost.(outliningresults)

Task5

Intheresultssection,thefocusisonoutliningtheresultswiththesupportofevidence,i.e.,figuresordata,etc.,whichareveryoftenpresentedinvisualelements.Incontrast,inthediscussionsection,thefocusisoninterpretingtheresultsinthecontextofexistingliterature,forexample,bycomparingtheresultsofthecurrentstudywiththoseofpreviousstudiestohighlightthesignificanceoftheresearchfindings.Theresearchresultsmaybementionedaswell,butinamuchmoreconcisemannerthanintheresultssection.

ChapterSixWritingtheDiscussion

Task1

Steps

Sentencenumbers(Extract1)

Sentencenumbers(Extract2)

Introductiontothe

mainfindings

①②③④⑤

①②③④⑤⑥⑦

Interpretation of

⑥⑦⑧⑨⑩⑪⑫

⑧⑨⑩⑪⑫⑬⑭⑮

theresultsinthe

contextofprevious

⑬⑭

⑯⑰⑱⑲⑳㉑㉒㉓

research

Implications andpotentialapplications of

findings

⑮

Limitations and

suggestions forfutureresearch

㉔㉕㉖㉗㉘㉙㉚㉛㉜

㉝㉞㉟㊱㊲㊳㊴

Summary of the

mainresearch

㊵㊶㊷㊸㊹㊺

Task2

Hedges:Itispossiblethat,couldbe

Function:Thesehedgesindicatethattheassociationisspeculativeandnotdefinitivelyproven,reducingthestrengthoftheclaim.

Hedges:Someevidencesuggests,mayexist

Function:Theuseof“some”limitsthescopeoftheevidence,while“mayexist”introducesthepossibilityofthecorrelationwithoutassertingitasafact.

Hedges:Ithasbeenproposed,mightleadto

Function:Thehedge“Ithasbeenproposed”attributestheideatootherresearchers,while“mightleadto”conveysapotentialoutcomethatisnotguaranteed.

Hedges:Insomestudies,hasbeenobserved

Function:“Insomestudies”qualifiestheresearchfindings,while“hasbeenobserved”impliesthatthelinkisnotaconsistentoruniversallyacceptedfact.

Hedges:Itcanbeargued,mightbe

Function:“Itcanbeargued”opensthedoorfordebateoralternativeviewpoints,and“mightbe”indicatesthatthecausalrelationshipisuncertainandspeculative.

Hedges:Thereissomeindication,couldpotentially

Function:“Thereissomeindication”suggeststhattheevidenceislimited,and“couldpotentially”expressesapossibilitythatisnotyetestablished.

Hedges:can,potentially

Function:Thewords“can”“potentially”areusedtohedgetheclaimabouttheimpactofDeepEnergy,suggestingthatthereductionisapossibleoutcomebasedonthestudy'sprojections.

Hedge:ingeneral

Function:Thephrase“Ingeneral”useshedgingtosuggestthattheauthors’claimisbroadlyapplicable,buttheremaybespecificcasesorcontextswherethisisnottrue.

Task3

1.–1.

2.–2.

3.–3.

ChapterSevenWritingtheConclusion

Task1

Moves

Sentence

numbers(example1)

Sentence

numbers(example2)

summaryofthepaper

①②③

①②③④

most important researchfindings

④⑤

⑤

significance of research

findings

⑥⑦

⑥

limitationsofthestudy

0

⑦⑧⑨⑪⑬

suggestions for futureresearch

⑧⑨

⑩⑫⑭

Task3

Theoriginalparagraphissummarizedeffectivelybyusingthefollowingskills:

Condensation:Theoriginalparagraphislengthyanddetailed,providinginformationaboutthestudypurpose,samplesize,surveyquestions,andspecificfindings.Thesummarycondensesthisinformationintoamoreconciseform,focusingonthekeyresultsandimplications,andcombinestheresearchresultswiththeirimplicationsintoonesentence:Increasedsocialmediaengagementwaslinkedtoshortersleepdurationsandloweracademicperformance,emphasizingtheharmfulconsequencesforcollegestudents.Whileintheoriginalparagraph,theresultsandimplicationsarepresentedseparatelyintwosentences.

Paraphrasing:Insteadofdirectlyquotingtheoriginaltext,thesummaryparaphrasesthekeyideasinanewandsuccinctmanner.Forexample,“Asampleof500undergraduatestudentswassurveyed”isparaphrasedas“Surveying500undergraduates”.

UseofConciseLanguage:Thesummaryemploysconciseandto-the-pointlanguagetocommunicatethemainideaswithoutunnecessarydetails.Forinstance,“StudentswhoreportedspendingmoretimeonsocialmediahadshortersleepdurationsandlowerGPAscomparedtothosewhospentlesstimeonsocialmedia”issummarizedas“Increasedsocialmediaengagementwaslinkedtoshortersleepdurationsandloweracademicperformance”.

4)

Task4

VersionAisbetterintermsofclarity,conciseness,anduseofpreciseacademiclanguage.Itisalsomoreaccurateinthatiteffectivelycapturestheessentialdefinitionofsustainabledevelopmentfromtheoriginalparagraphandconveystheconcept'scomplexityandimportancewithafocusonintergenerationalequityandtheintegrationofeconomic,social,andenvironmentalgoals.Thesummaryisstructuredinacohesivemanner,enhancingreadabilityandensuringthatthereadergraspsthekeypointswithoutunnecessarydetails.Moreover,itmaintainstheoriginalparagraph’sdepthofunderstandingandavoidsthevaguenessand

lessformallanguagepresentinVersionB,makingitamoreeffectiveandimpactfulrepresentationoftheoriginaltext.

Task5

①Inthispaper,wepresentedPHYSIOGANanovelmodelarchitecturetotraingenerativemodelsforphysiologicalsensorreadingsconditionedontheirclasslabels.②Comparedtootherbaselinemodelswhichweretrainedusingthevanillamaximumlikelihood,GANs,andVAEtrainingobjectives,PHYSIOGANproduceshigh-qualitysamplesthatattaingoodscoresinbothaccuracyanddiversity.③WeprovetheutilityofPHYSIOGANbyshowingitssignificantimprovementoverbaselinealgorithmsinproducingasyntheticdatasetthatwecanusetotrainclassificationmodelswithsignificantlyhighertestaccuracies.④Furthermore,weshowthatPHYSIOGANsurpassesexistingmethodsforsensordataimputationinfillingthemissingvalueswithrealisticvalues