HW5 Part II + Biasin NLP

HW5PartII+Bias inNLP

Spring2019CalebKaijiLu

HW5PartII:Debiasing WordEmbeddings[Bolukabasi 2016]

• Man:King::Woman:Queen• Paris:France::Tokyo:Japan

• He:Brother ::She:Sister• He:Blue ::She:Pink• He:Doctor ::She:Nurse• He:Realist ::She:Feminist• She:Pregnancy ::He:Kidney Stone• She:Baking::He:Roasting• She:Blonde::He:Blond

HW5PartII:Debiasing WordEmbeddings[Bolukabasi 2016]

• Tobereleasedtoday• Threesteps

• Identifygendersubspace(PCAusingSVD)• Neutralize• Equalize

• Evaluation• Analogycompletionforhe—she• AnalogycompletionforaWEevaluationdataset

• Threewordfiles:• Gender-definitionalwords(foridentifying thegendersubspace)• Gender-specificwords(foridentifyingwordstoneutralize)• Equalizedpairs(wordstoequalize)

TheGeometryofGender

she

he

her

his woman

man

female

maleMary

John

Gender(he-she) AxisFirst Principal Component

PrincipalComponentAnalysis

• PrincipalComponents(PC)areorthogonaldirectionsthatcapturemostofthevarianceinthedata.• 1st PC– direction ofgreatestvariabilityindata

• 2nd PC– Next orthogonal(uncorrelated)direction ofgreatestvariability(removeallvariability infirstdirection,thenfindnextdirectionofgreatestvariability)

• Andsoon…

PrincipalComponentAnalysis(PCA)

• Letv1,v2,…,vd denotethedprincipalcomponents.• Visorthonormal

• LetX=[x1,x2,…,xn](columnsarethedatapoints)• Datapointsarecentered

• Findvectorthatmaximizessamplevarianceofprojecteddata• Findvectorthatminimizes theaveragereconstruction error

PrincipalComponentAnalysis(PCA)

• Blackboard

Identify GenderSubspace

Neutralizeandequalize

NeutralizeandEqualize

• B:gendersubspace• w_B:projectionofwonB• BlackBoard

Agenda

• Introduction• GenderBiasinNLPtasks• CounterfactualData-Augmentation• GenderBiasinRNNLanguageModels• NeuralCoreference ResolutionBasics• GenderBiasinCoreference Resolution

NaturalQuestions• Doesbiasexistdownstreamtasks?• Doesmitigatingbiasinwordembeddings alsomitigatebiasinthedownstreamtasks?• Doesmitigatingbiasinwordembeddings impacttheperformanceofthedownstreamtasks?

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BiasinNLPtasks

• Biasinlanguagemodeling • BiasinCoreference resolution

BiasinNLPtasks[Lu,18]

• Definitionofbias:• CausalTesting• DefineMatchedpairsofindividuals(instances) thatdifferinonlyatargetedconcept(gender)

• Calculatedifference inoutcomes(conditional log-likelihood)

• Causal influence oftheconcept inthescrutinizedmodel

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Figure:TwomatchedPairs

BiasinNLPtasks[Lu,18]

• MatchedPairs• Templates: He/She isa/an|[OCCUPATION]• Aggregatetemplates• Aggregateoccupationwords(crosslistedfromUSlabordataandlanguagemodelvocabulary)

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Figure:TwomatchedPairs

HowtoEliminatetheBias

• Simplestsolution:Collectunbiaseddata• Notrealizable

• Changethemodelparameters/Changetheobjectivefunction

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Previously:Debiasing bychanging trainingobjective[Zhang,2018]

• Foreachanalogyinthedataset,weletx=(x1,x2,x3)• x1=he;x2=doctor;x3=doctor;x4=?

• OriginalModel(Lp)• Groundtruthforthefourthword• Estimate forthefourthword:

• AdversarialModel(LA)• Estimate forAdversarial network:• Ground truth forAdversarial Network:

Previously:Debiasing bychanging themodelparameters

• Debiasing theembeddinglayer?

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WordEmbeddings:TrainableorFixed?

WordEmbedding canbeusedtoreplacewordsasinputstothemodel• Pros

• Efficient• HandleOOVcases ifthetrainingdataset issmall

• Cons:• Cannottailortothetask

• Debiasing wordembeddingmaybehelpful

WordEmbedding canbetrainedaspartofthemodel• Pros:

• LearnUseful representations specific tothetask

• Cons:• Expensive• Datasetmight betoosmall tolearn useful

representations• Datasetmight notcover allthevocabularies

• DebiasingWordembeddingmaynotbehelpful• Destroy themodel• Biasisrelearned

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HowtoEliminatetheBias

• Simplestsolution:Collectunbiaseddata• Notrealizable

• Fixthemodel/Changetheobjectivefunction• Invasive,couldhurtperformance• Model-dependent

• SynthesizeUnbiaseddata• Model-agnostic• Counterfactual DataAugmentation

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Debiasing bySynthesizingdata:CounterfactualDataAugmentation

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• Generateanewsentencebyflippinggender-specificwordstotheircounterpartsofoppositegender• Addthenewsentencestothetrainingdata• Trainanewmodel

CounterfactualDataAugmentation

• Identifythelistofgenderedwordpairs• (he,she), (man,woman),(actor,actress), (monk,nun),(actors,actresses),…..

• Makesurethattheflippedsentencesaregrammaticallycorrect• “BillClinton’swifeisHillary."

• Can’tflip!BillClinton’shusband isHillary.• Rule:Ifthegenderedwordreferstothesameperson/entitywithapropernoun,weshallnotflip.

• Handleothercornercases• Ex:her(his/him)

• CouldbeappliedtootherNLPtasks

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Experiment1:LanguageModeling

• Models:• AbenchmarkLSTM

• Embeddingsize:1500• LSTMcellsize:1500

• Debiasing :• Debias thetrainedembedding[baseline]()• CDA(naïve):Flipeverygender-specificwordswithout anygrammaticalconstraints• CDA(grammar):CDA(naïve)+grammaticalconstraint• Initializetheembedding layerfrombaselineandtrainonaugmenteddataset()

• Data:• Wiki-text2dataset• 36718sentences, atleast7579sentences withonegender-specific word

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Results

• OccupationBias• Negativeoccupation bias:biased towardsfemale;Positiveoccupationbias:biased towardsmale• Thebiasintheoriginal model roughlyaligns withexpectations ongender-occupation stereotypesintherealworld

• ApplyingCDAconsistentlymitigatebiasforalmostalloccupations.

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Results

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• AOB:AggregateOccupationBias;TestPerp:TestPerplexity• BothCDAmitigatebiaswhilepreservingtheperformance

• CDA(naïve)hassurprisinglybetterperformance

Results

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• Apply wordembeddingdebiasing afterthemodel istrained()greatlyreducesbias,butalsodestroysthemodelperformance• Reasonforlowbias:lowvarianceoftheoutputscoredistribution

• Applywordembeddingdebiasing ()andcontinuetrainingontheaugmenteddataset:• Reintroducebiasback

BiasinCoreference Resolution

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Coreference ResolutionBasics

• Identifyallmentionsthatrefertothesamerealworldentity• Mentions:words/phrases thatreferstoarealentity intheworld• Antecedent ofamention:othermention/mentions thatprecedes saidmention,whichreferstothesame entity

Coreference ResolutioninTwoSteps

• Detectthementions(easy)

• Clusterthementions(hard)

AMentionRankingSystem[clark &Manning,2016]

• Assigneachmentionitshighestscoringcandidateantecedentaccordingtothemodel• DummyNAmentionallowsmodeltodecline linkingthecurrentmentiontoanything(“singleton”or“first”mention)




• TestTime:• Clusterthepairs

NeuralCoref Model[clark &Manning,2016]

CDAforNeuralCoref Resolution


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• OccupationBias• Negativeoccupation bias:biased towardsfemale;Positiveoccupationbias:biased towardsmale• Thebiasintheoriginal model roughlyaligns withexpectations ongender-occupation stereotypesintherealworld

• ApplyingCDAconsistentlymitigatebiasforalmostalloccupations.


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• Pretrained Embedding(Word2vec)• AdditiveEffectof:

• Fixingtheembeddings usingdebiasing• Fixingotherparametersusingcounterfactualdataaugmentation


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Summary

• Genderbiasexistsindownstreamtasks• LanguageModels• Coreference Resolution

• Caneffectivelyreducebiasbytrainingonaugmenteddataset• Previousmethodsofaddressingbiasinwordembeddings

• Hurtsperformance ifdoneafteramodel istrained• Reintroduces thebiasbackifinitialized beforeamodel istrained• Additiveeffect iftheembedding ispretrained

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Questions?

• References:• T.Bolukbasi,K.-W.Chang,J.Y.Zou,V.Saligrama,andA.T.Kalai,“Manistocomputerprogrammeraswomanistohomemaker? debiasing wordembeddings,” inAdvances inneuralinformationprocessingsystems, 2016,pp.4349–4357.

• K.Lu,P.Mardziel,F.Wu,P.Amancharla,andA.Datta,“Genderbias inneuralnaturallanguage processing,”arXiv preprintarXiv:1807.11714,2018.

• KevinClarkandChristopherDManning.Deepreinforcement learningformention-ranking coreference models.arXiv preprintarXiv:1609.08667,2016b.

• KentonLee,Luheng He,MikeLewis,andLukeZettlemoyer. End-to-endneuralcoreference resolution.arXiv preprintarXiv:1707.07045,2017.

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HW5 Part II + Biasin NLP

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