Using INLINEFORM0 and INLINEFORM1
ABSTRACTIVE
Unanswerable
NONE
Results for the experiments are listed in Table TABREF12 . It is interesting to observe that the baseline model amplifies the bias in the training data set as measured by INLINEFORM0 and INLINEFORM1 . From measurements using the described bias metrics, our method effectively mitigates bias in language modelling without a significant increase in perplexity. At INLINEFORM2 value of 1, it reduces INLINEFORM3 by 58.95%, INLINEFORM4 by 45.74%, INLINEFORM5 by 100%, INLINEFORM6 by 98.52% and INLINEFORM7 by 98.98%. Compared to the results of CDA and REG, it achieves the best results in both occupation biases, INLINEFORM8 and INLINEFORM9 , and INLINEFORM10 . We notice that all methods result in INLINEFORM11 around 1, indicating that there are near equal amounts of female and male words in the generated texts. In our experiments we note that with increasing INLINEFORM12 , the bias steadily decreases and perplexity tends to slightly increase. This indicates that there is a trade-off between bias and perplexity.
1905.12801
Introduction </s> Natural ĠLanguage ĠProcessing Ġ( N LP ) Ġmodels Ġare Ġshown Ġto Ġcapture Ġunwanted Ġbiases Ġand Ġstereotypes Ġfound Ġin Ġthe Ġtraining Ġdata Ġwhich Ġraise Ġconcerns Ġabout Ġsocioeconomic , Ġethnic Ġand Ġgender Ġdiscrimination Ġwhen Ġthese Ġmodels Ġare Ġdeployed Ġfor Ġpublic Ġuse ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ. </s> There Ġare Ġnumerous Ġstudies Ġthat Ġidentify Ġalgorith mic Ġbias Ġin ĠN LP Ġapplications . ĠB IB REF 2 Ġshowed Ġethnic Ġbias Ġin ĠGoogle Ġaut ocom plete Ġsuggestions Ġwhereas ĠB IB REF 3 Ġfound Ġgender Ġbias Ġin Ġadvertisement Ġdelivery Ġsystems . ĠAdditionally , ĠB IB REF 1 Ġdemonstrated Ġthat Ġcore ference Ġresolution Ġsystems Ġexhibit Ġgender Ġbias . </s> Language Ġmodelling Ġis Ġa Ġpivotal Ġtask Ġin ĠN LP Ġwith Ġimportant Ġdownstream Ġapplications Ġsuch Ġas Ġtext Ġgeneration ĠB IB REF 4 Ġ. ĠRecent Ġstudies Ġby ĠB IB REF 0 Ġand ĠB IB REF 5 Ġhave Ġshown Ġthat Ġthis Ġtask Ġis Ġvulnerable Ġto Ġgender Ġbias Ġin Ġthe Ġtraining Ġcorpus . ĠTwo Ġprior Ġworks Ġfocused Ġon Ġreducing Ġbias Ġin Ġlanguage Ġmodelling Ġby Ġdata Ġpre processing ĠB IB REF 0 Ġand Ġword Ġembed ding Ġdeb i asing ĠB IB REF 5 Ġ. ĠIn Ġthis Ġstudy , Ġwe Ġinvestigate Ġthe Ġefficacy Ġof Ġbias Ġreduction Ġduring Ġtraining Ġby Ġintroducing Ġa Ġnew Ġloss Ġfunction Ġwhich Ġencourages Ġthe Ġlanguage Ġmodel Ġto Ġequal ize Ġthe Ġprobabilities Ġof Ġpredicting Ġg endered Ġword Ġpairs Ġlike Ġhe Ġand Ġshe . ĠAlthough Ġwe Ġrecognize Ġthat Ġgender Ġis Ġnon - binary , Ġfor Ġthe Ġpurpose Ġof Ġthis Ġstudy , Ġwe Ġfocus Ġon Ġfemale Ġand Ġmale Ġwords . </s> Our Ġmain Ġcontributions Ġare Ġsummarized Ġas Ġfollows : Ġi ) Ġto Ġour Ġbest Ġknowledge , Ġthis Ġstudy Ġis Ġthe Ġfirst Ġone Ġto Ġinvestigate Ġbias Ġallev iation Ġin Ġtext Ġgeneration Ġby Ġdirect Ġmodification Ġof Ġthe Ġloss Ġfunction ; Ġii ) Ġour Ġnew Ġloss Ġfunction Ġeffectively Ġreduces Ġgender Ġbias Ġin Ġthe Ġlanguage Ġmodels Ġduring Ġtraining Ġby Ġequal izing Ġthe Ġprobabilities Ġof Ġmale Ġand Ġfemale Ġwords Ġin Ġthe Ġoutput ; Ġiii ) Ġwe Ġshow Ġthat Ġend - to - end Ġdeb i asing Ġof Ġthe Ġlanguage Ġmodel Ġcan Ġachieve Ġword Ġembed ding Ġdeb i asing ; Ġiv ) Ġwe Ġprovide Ġan Ġinterpretation Ġof Ġour Ġresults Ġand Ġdraw Ġa Ġcomparison Ġto Ġother Ġexisting Ġdeb i asing Ġmethods . ĠWe Ġshow Ġthat Ġour Ġmethod , Ġcombined Ġwith Ġan Ġexisting Ġmethod , Ġcounter fact ual Ġdata Ġaug mentation , Ġachieves Ġthe Ġbest Ġresult Ġand Ġoutper forms Ġall Ġexisting Ġmethods . </s> Related ĠWork </s> Recently , Ġthe Ġstudy Ġof Ġbias Ġin ĠN LP Ġapplications Ġhas Ġreceived Ġincreasing Ġattention Ġfrom Ġresearchers . ĠMost Ġrelevant Ġwork Ġin Ġthis Ġdomain Ġcan Ġbe Ġbroadly Ġdivided Ġinto Ġtwo Ġcategories : Ġword Ġembed ding Ġdeb i asing Ġand Ġdata Ġdeb i asing Ġby Ġpre processing . </s> Dat as et </s> For Ġthe Ġtraining Ġdata , Ġwe Ġuse ĠDaily ĠMail Ġnews Ġarticles Ġreleased Ġby ĠB IB REF 9 Ġ. ĠThis Ġdataset Ġis Ġcomposed Ġof Ġ219 , 506 Ġarticles Ġcovering Ġa Ġdiverse Ġrange Ġof Ġtopics Ġincluding Ġbusiness , Ġsports , Ġtravel , Ġetc ., Ġand Ġis Ġclaimed Ġto Ġbe Ġbiased Ġand Ġsensational ĠB IB REF 5 Ġ. ĠFor Ġmanage ability , Ġwe Ġrandomly Ġsubs ample Ġ5 % Ġof Ġthe Ġtext . ĠThe Ġsubs ample Ġhas Ġaround Ġ8 . 25 Ġmillion Ġtokens Ġin Ġtotal . </s> Language ĠModel </s> We Ġuse Ġa Ġpre - trained Ġ300 - dimensional Ġword Ġembed ding , ĠGl o Ve , Ġby ĠB IB REF 10 Ġ. ĠWe Ġapply Ġrandom Ġsearch Ġto Ġthe Ġhyper param eter Ġtuning Ġof Ġthe ĠL ST M Ġlanguage Ġmodel . ĠThe Ġbest Ġhyper param eters Ġare Ġas Ġfollows : Ġ2 Ġhidden Ġlayers Ġeach Ġwith Ġ300 Ġunits , Ġa Ġsequence Ġlength Ġof Ġ35 , Ġa Ġlearning Ġrate Ġof Ġ20 Ġwith Ġan Ġan ne aling Ġschedule Ġof Ġdecay Ġstarting Ġfrom Ġ0 . 25 Ġto Ġ0 . 95 , Ġa Ġdrop out Ġrate Ġof Ġ0 . 25 Ġand Ġa Ġgradient Ġclip Ġof Ġ0 . 25 . ĠWe Ġtrain Ġour Ġmodels Ġfor Ġ150 Ġepoch s , Ġuse Ġa Ġbatch Ġsize Ġof Ġ48 , Ġand Ġset Ġearly Ġstopping Ġwith Ġa Ġpatience Ġof Ġ5 . </s> L oss ĠFunction </s> Language Ġmodels Ġare Ġusually Ġtrained Ġusing Ġcross - ent ropy Ġloss . ĠCross - ent ropy Ġloss Ġat Ġtime Ġstep ĠIN LINE FORM 0 Ġis ĠIN LINE FORM 1 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġvocabulary , ĠIN LINE FORM 1 Ġis Ġthe Ġone Ġhot Ġvector Ġof Ġground Ġtruth Ġand ĠIN LINE FORM 2 Ġindicates Ġthe Ġoutput Ġsoft max Ġprobability Ġof Ġthe Ġmodel . </s> We Ġintroduce Ġa Ġloss Ġterm ĠIN LINE FORM 0 Ġ, Ġwhich Ġaims Ġto Ġequal ize Ġthe Ġpredicted Ġprobabilities Ġof Ġgender Ġpairs Ġsuch Ġas Ġwoman Ġand Ġman . ĠIN LINE FORM 1 </s> IN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare Ġa Ġset Ġof Ġcorresponding Ġgender Ġpairs , ĠIN LINE FORM 2 Ġis Ġthe Ġsize Ġof Ġthe Ġgender Ġpairs Ġset , Ġand ĠIN LINE FORM 3 Ġindicates Ġthe Ġoutput Ġsoft max Ġprobability . ĠWe Ġuse Ġgender Ġpairs Ġprovided Ġby ĠB IB REF 7 Ġ. ĠBy Ġconsidering Ġonly Ġgender Ġpairs Ġwe Ġensure Ġthat Ġonly Ġgender Ġinformation Ġis Ġneutral ized Ġand Ġdistribution Ġover Ġsemantic Ġconcepts Ġis Ġnot Ġaltered . ĠFor Ġexample , Ġit Ġwill Ġtry Ġto Ġequal ize Ġthe Ġprobabilities Ġof Ġcongressman Ġwith Ġcongress woman Ġand Ġactor Ġwith Ġactress Ġbut Ġdistribution Ġof Ġcongressman , Ġcongress woman Ġversus Ġactor , Ġactress Ġwill Ġnot Ġbe Ġaffected . ĠOverall Ġloss Ġcan Ġbe Ġwritten Ġas ĠIN LINE FORM 4 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġhyper param eter Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġcorpus Ġsize . ĠWe Ġobserve Ġthat Ġamong Ġthe Ġsimilar Ġmin ima Ġof Ġthe Ġloss Ġfunction , ĠIN LINE FORM 2 Ġencourages Ġthe Ġmodel Ġto Ġconverge Ġtowards Ġa Ġminimum Ġthat Ġexhibits Ġthe Ġlowest Ġgender Ġbias . </s> Model ĠEvaluation </s> Language Ġmodels Ġare Ġevaluated Ġusing Ġperplex ity , Ġwhich Ġis Ġa Ġstandard Ġmeasure Ġof Ġperformance Ġfor Ġunseen Ġdata . ĠFor Ġbias Ġevaluation , Ġwe Ġuse Ġan Ġarray Ġof Ġmetrics Ġto Ġprovide Ġa Ġholistic Ġdiagnosis Ġof Ġthe Ġmodel Ġbehavior Ġunder Ġdeb i asing Ġtreatment . ĠThese Ġmetrics Ġare Ġdiscussed Ġin Ġdetail Ġbelow . ĠIn Ġall Ġthe Ġevaluation Ġmetrics Ġrequiring Ġgender Ġpairs , Ġwe Ġuse Ġgender Ġpairs Ġprovided Ġby ĠB IB REF 7 Ġ. ĠThis Ġlist Ġcontains Ġ223 Ġpairs , Ġall Ġother Ġwords Ġare Ġconsidered Ġgender - neutral . </s> Co - occ urrence Ġbias Ġis Ġcomputed Ġfrom Ġthe Ġmodel - generated Ġtexts Ġby Ġcomparing Ġthe Ġoccurrences Ġof Ġall Ġgender - neutral Ġwords Ġwith Ġfemale Ġand Ġmale Ġwords . ĠA Ġword Ġis Ġconsidered Ġto Ġbe Ġbiased Ġtowards Ġa Ġcertain Ġgender Ġif Ġit Ġoccurs Ġmore Ġfrequently Ġwith Ġwords Ġof Ġthat Ġgender . ĠThis Ġdefinition Ġwas Ġfirst Ġused Ġby ĠB IB REF 7 Ġand Ġlater Ġadapted Ġby ĠB IB REF 5 Ġ. ĠUsing Ġthe Ġdefinition Ġof Ġgender Ġbias Ġsimilar Ġto Ġthe Ġone Ġused Ġby ĠB IB REF 5 Ġ, Ġwe Ġdefine Ġgender Ġbias Ġas ĠIN LINE FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġset Ġof Ġgender - neutral Ġwords , Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġoccurrences Ġof Ġa Ġword ĠIN LINE FORM 2 Ġwith Ġwords Ġof Ġgender ĠIN LINE FORM 3 Ġin Ġthe Ġsame Ġwindow . ĠThis Ġscore Ġis Ġdesigned Ġto Ġcapture Ġunequal Ġco - occ ur rences Ġof Ġneutral Ġwords Ġwith Ġmale Ġand Ġfemale Ġwords . ĠCo - occ ur rences Ġare Ġcomputed Ġusing Ġa Ġsliding Ġwindow Ġof Ġsize Ġ10 Ġextending Ġequally Ġin Ġboth Ġdirections . ĠFurthermore , Ġwe Ġonly Ġconsider Ġwords Ġthat Ġoccur Ġmore Ġthan Ġ20 Ġtimes Ġwith Ġg endered Ġwords Ġto Ġexclude Ġrandom Ġeffects . </s> We Ġalso Ġevaluate Ġa Ġnormalized Ġversion Ġof ĠIN LINE FORM 0 Ġwhich Ġwe Ġdenote Ġby Ġconditional Ġco - occ urrence Ġbias , ĠIN LINE FORM 1 Ġ. ĠThis Ġis Ġdefined Ġas ĠIN LINE FORM 2 </s> where ĠIN LINE FORM 0 </s> IN LINE FORM 0 Ġis Ġless Ġaffected Ġby Ġthe Ġdisparity Ġin Ġthe Ġgeneral Ġdistribution Ġof Ġmale Ġand Ġfemale Ġwords Ġin Ġthe Ġtext . ĠThe Ġdisparity Ġbetween Ġthe Ġoccurrences Ġof Ġthe Ġtwo Ġgenders Ġmeans Ġthat Ġtext Ġis Ġmore Ġinclined Ġto Ġmention Ġone Ġover Ġthe Ġother , Ġso Ġit Ġcan Ġalso Ġbe Ġconsidered Ġa Ġform Ġof Ġbias . ĠWe Ġreport Ġthe Ġratio Ġof Ġoccurrence Ġof Ġmale Ġand Ġfemale Ġwords Ġin Ġthe Ġmodel Ġgenerated Ġtext , ĠIN LINE FORM 1 Ġ, Ġas ĠIN LINE FORM 2 </s> Another Ġway Ġof Ġquant ifying Ġbias Ġin ĠN LP Ġmodels Ġis Ġbased Ġon Ġthe Ġidea Ġof Ġcausal Ġtesting . ĠThe Ġmodel Ġis Ġexposed Ġto Ġpaired Ġsamples Ġwhich Ġdiffer Ġonly Ġin Ġone Ġattribute Ġ( e . g . Ġgender ) Ġand Ġthe Ġdisparity Ġin Ġthe Ġoutput Ġis Ġinterpreted Ġas Ġbias Ġrelated Ġto Ġthat Ġattribute . ĠB IB REF 1 Ġand ĠB IB REF 0 Ġapplied Ġthis Ġmethod Ġto Ġmeasure Ġbias Ġin Ġcore ference Ġresolution Ġand ĠB IB REF 0 Ġalso Ġused Ġit Ġfor Ġevaluating Ġgender Ġbias Ġin Ġlanguage Ġmodelling . </s> Following Ġthe Ġapproach Ġsimilar Ġto ĠB IB REF 0 Ġ, Ġwe Ġlimit Ġthis Ġbias Ġevaluation Ġto Ġa Ġset Ġof Ġgender - neutral Ġoccupations . ĠWe Ġcreate Ġa Ġlist Ġof Ġsentences Ġbased Ġon Ġa Ġset Ġof Ġtemplates . ĠThere Ġare Ġtwo Ġsets Ġof Ġtemplates Ġused Ġfor Ġevaluating Ġcausal Ġoccupation Ġbias Ġ( Table ĠT AB REF 7 Ġ). ĠThe Ġfirst Ġset Ġof Ġtemplates Ġis Ġdesigned Ġto Ġmeasure Ġhow Ġthe Ġprobabilities Ġof Ġoccupation Ġwords Ġdepend Ġon Ġthe Ġgender Ġinformation Ġin Ġthe Ġseed . ĠBelow Ġis Ġan Ġexample Ġof Ġthe Ġfirst Ġset Ġof Ġtemplates : ĠIN LINE FORM 0 </s> Here , Ġthe Ġvertical Ġbar Ġseparates Ġthe Ġseed Ġsequence Ġthat Ġis Ġfed Ġinto Ġthe Ġlanguage Ġmodels Ġfrom Ġthe Ġtarget Ġoccupation , Ġfor Ġwhich Ġwe Ġobserve Ġthe Ġoutput Ġsoft max Ġprobability . ĠWe Ġmeasure Ġcausal Ġoccupation Ġbias Ġconditioned Ġon Ġgender Ġas ĠIN LINE FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġset Ġof Ġgender - neutral Ġoccupations Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġsize Ġof Ġthe Ġgender Ġpairs Ġset . ĠFor Ġexample , ĠIN LINE FORM 2 Ġis Ġthe Ġsoft max Ġprobability Ġof Ġthe Ġword ĠIN LINE FORM 3 Ġwhere Ġthe Ġseed Ġsequence Ġis ĠHe Ġis Ġa . ĠThe Ġsecond Ġset Ġof Ġtemplates Ġlike Ġbelow , Ġaims Ġto Ġcapture Ġhow Ġthe Ġprobabilities Ġof Ġg endered Ġwords Ġdepend Ġon Ġthe Ġoccupation Ġwords Ġin Ġthe Ġseed . ĠIN LINE FORM 4 </s> Ca usal Ġoccupation Ġbias Ġconditioned Ġon Ġoccupation Ġis Ġrepresented Ġas ĠIN LINE FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġset Ġof Ġgender - neutral Ġoccupations Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġsize Ġof Ġthe Ġgender Ġpairs Ġset . ĠFor Ġexample , ĠIN LINE FORM 2 Ġis Ġthe Ġsoft max Ġprobability Ġof Ġman Ġwhere Ġthe Ġseed Ġsequence Ġis ĠThe Ġdoctor Ġis Ġa . </s> We Ġbelieve Ġthat Ġboth ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġcontribute Ġto Ġgender Ġbias Ġin Ġthe Ġmodel - generated Ġtexts . ĠWe Ġalso Ġnote Ġthat ĠIN LINE FORM 2 Ġis Ġmore Ġeasily Ġinfluenced Ġby Ġthe Ġgeneral Ġdisparity Ġin Ġmale Ġand Ġfemale Ġword Ġprobabilities . </s> Our Ġdeb i asing Ġapproach Ġdoes Ġnot Ġexplicitly Ġaddress Ġthe Ġbias Ġin Ġthe Ġembed ding Ġlayer . ĠTherefore , Ġwe Ġuse Ġgender - neutral Ġoccupations Ġto Ġmeasure Ġthe Ġembed ding Ġbias Ġto Ġobserve Ġif Ġdeb i asing Ġthe Ġoutput Ġlayer Ġalso Ġdecreases Ġthe Ġbias Ġin Ġthe Ġembed ding . ĠWe Ġdefine Ġthe Ġembed ding Ġbias , ĠIN LINE FORM 0 Ġ, Ġas Ġthe Ġdifference Ġbetween Ġthe ĠEucl ide an Ġdistance Ġof Ġan Ġoccupation Ġword Ġto Ġmale Ġwords Ġand Ġthe Ġdistance Ġof Ġthe Ġoccupation Ġword Ġto Ġthe Ġfemale Ġcounterparts . ĠThis Ġdefinition Ġis Ġequivalent Ġto Ġbias Ġby Ġprojection Ġdescribed Ġby ĠB IB REF 6 Ġ. ĠWe Ġdefine ĠIN LINE FORM 1 Ġas ĠIN LINE FORM 2 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġset Ġof Ġgender - neutral Ġoccupations , ĠIN LINE FORM 1 Ġis Ġthe Ġsize Ġof Ġthe Ġgender Ġpairs Ġset Ġand ĠIN LINE FORM 2 Ġis Ġthe Ġword - to - vector Ġdictionary . </s> Ex isting ĠAppro aches </s> We Ġapply ĠC DA Ġwhere Ġwe Ġswap Ġall Ġthe Ġg endered Ġwords Ġusing Ġa Ġbid irection al Ġdictionary Ġof Ġgender Ġpairs Ġdescribed Ġby ĠB IB REF 0 Ġ. ĠThis Ġcreates Ġa Ġdataset Ġtwice Ġthe Ġsize Ġof Ġthe Ġoriginal Ġdata , Ġwith Ġexactly Ġthe Ġsame Ġcontextual Ġdistributions Ġfor Ġboth Ġgenders Ġand Ġwe Ġuse Ġit Ġto Ġtrain Ġthe Ġlanguage Ġmodels . </s> We Ġalso Ġimplement Ġthe Ġbias Ġregular ization Ġmethod Ġof ĠB IB REF 5 Ġwhich Ġdeb i ases Ġthe Ġword Ġembed ding Ġduring Ġlanguage Ġmodel Ġtraining Ġby Ġminimizing Ġthe Ġprojection Ġof Ġneutral Ġwords Ġon Ġthe Ġgender Ġaxis . ĠWe Ġuse Ġhyper param eter Ġtuning Ġto Ġfind Ġthe Ġbest Ġregular ization Ġcoefficient Ġand Ġreport Ġresults Ġfrom Ġthe Ġmodel Ġtrained Ġwith Ġthis Ġcoefficient . ĠWe Ġlater Ġrefer Ġto Ġthis Ġstrategy Ġas ĠREG . </s> Exper iments </s> Initially , Ġwe Ġmeasure Ġthe Ġco - occ urrence Ġbias Ġin Ġthe Ġtraining Ġdata . ĠAfter Ġtraining Ġthe Ġbaseline Ġmodel , Ġwe Ġimplement Ġour Ġloss Ġfunction Ġand Ġtune Ġfor Ġthe ĠIN LINE FORM 0 Ġhyper param eter . ĠWe Ġtest Ġthe Ġexisting Ġdeb i asing Ġapproaches , ĠC DA Ġand ĠREG , Ġas Ġwell Ġbut Ġsince ĠB IB REF 5 Ġreported Ġthat Ġresults Ġfluct uate Ġsubstantially Ġwith Ġdifferent ĠREG Ġregular ization Ġcoefficients , Ġwe Ġperform Ġhyper param eter Ġtuning Ġand Ġreport Ġthe Ġbest Ġresults Ġin ĠTable ĠT AB REF 12 Ġ. ĠAdditionally , Ġwe Ġimplement Ġa Ġcombination Ġof Ġour Ġloss Ġfunction Ġand ĠC DA Ġand Ġtune Ġfor ĠIN LINE FORM 1 Ġ. ĠFinally , Ġbias Ġevaluation Ġis Ġperformed Ġfor Ġall Ġthe Ġtrained Ġmodels . ĠCa usal Ġoccupation Ġbias Ġis Ġmeasured Ġdirectly Ġfrom Ġthe Ġmodels Ġusing Ġtemplate Ġdatasets Ġdiscussed Ġabove Ġand Ġco - occ urrence Ġbias Ġis Ġmeasured Ġfrom Ġthe Ġmodel - generated Ġtexts , Ġwhich Ġconsist Ġof Ġ10 , 000 Ġdocuments Ġof Ġ500 Ġwords Ġeach . </s> Results </s> Results Ġfor Ġthe Ġexperiments Ġare Ġlisted Ġin ĠTable ĠT AB REF 12 Ġ. ĠIt Ġis Ġinteresting Ġto Ġobserve Ġthat Ġthe Ġbaseline Ġmodel Ġampl ifies Ġthe Ġbias Ġin Ġthe Ġtraining Ġdata Ġset Ġas Ġmeasured Ġby ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġ. ĠFrom Ġmeasurements Ġusing Ġthe Ġdescribed Ġbias Ġmetrics , Ġour Ġmethod Ġeffectively Ġmit ig ates Ġbias Ġin Ġlanguage Ġmodelling Ġwithout Ġa Ġsignificant Ġincrease Ġin Ġperplex ity . ĠAt ĠIN LINE FORM 2 Ġvalue Ġof Ġ1 , Ġit Ġreduces ĠIN LINE FORM 3 Ġby Ġ58 . 95 %, ĠIN LINE FORM 4 Ġby Ġ45 . 74 %, ĠIN LINE FORM 5 Ġby Ġ100 %, ĠIN LINE FORM 6 Ġby Ġ98 . 52 % Ġand ĠIN LINE FORM 7 Ġby Ġ98 . 98 %. ĠCompared Ġto Ġthe Ġresults Ġof ĠC DA Ġand ĠREG , Ġit Ġachieves Ġthe Ġbest Ġresults Ġin Ġboth Ġoccupation Ġbiases , ĠIN LINE FORM 8 Ġand ĠIN LINE FORM 9 Ġ, Ġand ĠIN LINE FORM 10 Ġ. ĠWe Ġnotice Ġthat Ġall Ġmethods Ġresult Ġin ĠIN LINE FORM 11 Ġaround Ġ1 , Ġindicating Ġthat Ġthere Ġare Ġnear Ġequal Ġamounts Ġof Ġfemale Ġand Ġmale Ġwords Ġin Ġthe Ġgenerated Ġtexts . ĠIn Ġour Ġexperiments Ġwe Ġnote Ġthat Ġwith Ġincreasing ĠIN LINE FORM 12 Ġ, Ġthe Ġbias Ġsteadily Ġdecreases Ġand Ġperplex ity Ġtends Ġto Ġslightly Ġincrease . ĠThis Ġindicates Ġthat Ġthere Ġis Ġa Ġtrade - off Ġbetween Ġbias Ġand Ġperplex ity . </s> REG Ġis Ġnot Ġvery Ġeffective Ġin Ġmitigating Ġbias Ġwhen Ġcompared Ġto Ġother Ġmethods , Ġand Ġfails Ġto Ġachieve Ġthe Ġbest Ġresult Ġin Ġany Ġof Ġthe Ġbias Ġmetrics Ġthat Ġwe Ġused . ĠBut ĠREG Ġresults Ġin Ġthe Ġbest Ġperplex ity Ġand Ġeven Ġdoes Ġbetter Ġthan Ġthe Ġbaseline Ġmodel Ġin Ġthis Ġrespect . ĠThis Ġindicates Ġthat ĠREG Ġhas Ġa Ġslight Ġregular ization Ġeffect . ĠAdditionally , Ġit Ġis Ġinteresting Ġto Ġnote Ġthat Ġour Ġloss Ġfunction Ġoutper forms ĠREG Ġin ĠIN LINE FORM 0 Ġeven Ġthough ĠREG Ġexplicitly Ġaims Ġto Ġreduce Ġgender Ġbias Ġin Ġthe Ġembed d ings . ĠAlthough Ġour Ġmethod Ġdoes Ġnot Ġexplicitly Ġattempt Ġgeometric Ġdeb i asing Ġof Ġthe Ġword Ġembed ding , Ġthe Ġresults Ġshow Ġthat Ġit Ġresults Ġin Ġthe Ġmost Ġdeb i ased Ġembed ding Ġas Ġcompared Ġto Ġother Ġmethods . ĠFurthermore , ĠB IB REF 8 Ġemphasizes Ġthat Ġgeometric Ġgender Ġbias Ġin Ġword Ġembed d ings Ġis Ġnot Ġcompletely Ġunderstood Ġand Ġexisting Ġword Ġembed ding Ġdeb i asing Ġstrategies Ġare Ġinsufficient . ĠOur Ġapproach Ġprovides Ġan Ġappealing Ġend - to - end Ġsolution Ġfor Ġmodel Ġdeb i asing Ġwithout Ġrelying Ġon Ġany Ġmeasure Ġof Ġbias Ġin Ġthe Ġword Ġembed ding . ĠWe Ġbelieve Ġthis Ġconcept Ġis Ġgeneral izable Ġto Ġother ĠN LP Ġapplications . </s> Our Ġmethod Ġoutper forms ĠC DA Ġin ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ, Ġand ĠIN LINE FORM 2 Ġ. ĠWhile ĠC DA Ġachieves Ġslightly Ġbetter Ġresults Ġfor Ġco - occ urrence Ġbiases , ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġ, Ġand Ġresults Ġin Ġa Ġbetter Ġperplex ity . ĠWith Ġa Ġmarginal Ġdifferences , Ġour Ġresults Ġare Ġcomparable Ġto Ġthose Ġof ĠC DA Ġand Ġboth Ġmodels Ġseem Ġto Ġhave Ġsimilar Ġbias Ġmitigation Ġeffects . ĠHowever , Ġour Ġmethod Ġdoes Ġnot Ġrequire Ġa Ġdata Ġaug mentation Ġstep Ġand Ġallows Ġtraining Ġof Ġan Ġunbiased Ġmodel Ġdirectly Ġfrom Ġbiased Ġdatasets . ĠFor Ġthis Ġreason , Ġit Ġalso Ġrequires Ġless Ġtime Ġto Ġtrain Ġthan ĠC DA Ġsince Ġits Ġtraining Ġdata Ġhas Ġa Ġsmaller Ġsize Ġwithout Ġdata Ġaug mentation . ĠFurthermore , ĠC DA Ġfails Ġto Ġeffectively Ġmitigate Ġoccupation Ġbias Ġwhen Ġcompared Ġto Ġour Ġapproach . ĠAlthough Ġthe Ġtraining Ġdata Ġfor ĠC DA Ġdoes Ġnot Ġcontain Ġgender Ġbias , Ġthe Ġmodel Ġstill Ġexhibits Ġsome Ġgender Ġbias Ġwhen Ġmeasured Ġwith Ġour Ġcausal Ġoccupation Ġbias Ġmetrics . ĠThis Ġreinforces Ġthe Ġconcept Ġthat Ġsome Ġmodel - level Ġconstraints Ġare Ġessential Ġto Ġdeb i asing Ġa Ġmodel Ġand Ġdataset Ġdeb i asing Ġalone Ġcannot Ġbe Ġtrusted . </s> Finally , Ġwe Ġnote Ġthat Ġthe Ġcombination Ġof ĠC DA Ġand Ġour Ġloss Ġfunction Ġoutper forms Ġall Ġthe Ġmethods Ġin Ġall Ġmeasures Ġof Ġbiases Ġwithout Ġcompromising Ġperplex ity . ĠTherefore , Ġit Ġcan Ġbe Ġargued Ġthat Ġa Ġcascade Ġof Ġthese Ġapproaches Ġcan Ġbe Ġused Ġto Ġoptim ally Ġdeb ias Ġthe Ġlanguage Ġmodels . </s> Conclusion Ġand ĠDiscussion </s> In Ġthis Ġresearch , Ġwe Ġpropose Ġa Ġnew Ġapproach Ġfor Ġmitigating Ġgender Ġbias Ġin Ġneural Ġlanguage Ġmodels Ġand Ġempir ically Ġshow Ġits Ġeffectiveness Ġin Ġreducing Ġbias Ġas Ġmeasured Ġwith Ġdifferent Ġevaluation Ġmetrics . ĠOur Ġresearch Ġalso Ġhighlights Ġthe Ġfact Ġthat Ġdeb i asing Ġthe Ġmodel Ġwith Ġbias Ġpenalties Ġin Ġthe Ġloss Ġfunction Ġis Ġan Ġeffective Ġmethod . ĠWe Ġemphasize Ġthat Ġloss Ġfunction Ġbased Ġdeb i asing Ġis Ġpowerful Ġand Ġgeneral izable Ġto Ġother Ġdownstream ĠN LP Ġapplications . ĠThe Ġresearch Ġalso Ġreinforces Ġthe Ġidea Ġthat Ġgeometric Ġdeb i asing Ġof Ġthe Ġword Ġembed ding Ġis Ġnot Ġa Ġcomplete Ġsolution Ġfor Ġdeb i asing Ġthe Ġdownstream Ġapplications Ġbut Ġencourages Ġend - to - end Ġapproaches Ġto Ġdeb i asing . </s> All Ġthe Ġdeb i asing Ġtechniques Ġexperimented Ġin Ġthis Ġpaper Ġrely Ġon Ġa Ġpred efined Ġset Ġof Ġgender Ġpairs Ġin Ġsome Ġway . ĠC DA Ġused Ġgender Ġpairs Ġfor Ġflipping , ĠREG Ġuses Ġit Ġfor Ġgender Ġspace Ġdefinition Ġand Ġour Ġtechnique Ġuses Ġthem Ġfor Ġcomputing Ġloss . ĠThis Ġreliance Ġon Ġpre - defined Ġset Ġof Ġgender Ġpairs Ġcan Ġbe Ġconsidered Ġa Ġlimitation Ġof Ġthese Ġmethods . ĠIt Ġalso Ġresults Ġin Ġanother Ġconcern . ĠThere Ġare Ġgender Ġassociated Ġwords Ġwhich Ġdo Ġnot Ġhave Ġpairs , Ġlike Ġpregnant . ĠThese Ġwords Ġare Ġnot Ġtreated Ġproperly Ġby Ġtechniques Ġrelying Ġon Ġgender Ġpairs . </s> Future Ġwork Ġincludes Ġdesigning Ġa Ġcontext - aware Ġversion Ġof Ġour Ġloss Ġfunction Ġwhich Ġcan Ġdistinguish Ġbetween Ġthe Ġunbiased Ġand Ġbiased Ġmentions Ġof Ġthe Ġg endered Ġwords Ġand Ġonly Ġpenal ize Ġthe Ġbiased Ġversion . ĠAnother Ġinteresting Ġdirection Ġis Ġexploring Ġthe Ġapplication Ġof Ġthis Ġmethod Ġin Ġmitigating Ġracial Ġbias Ġwhich Ġbrings Ġmore Ġchallenges . </s> Acknowled gment </s> We Ġare Ġgrateful Ġto ĠSam ĠBowman Ġfor Ġhelpful Ġadvice , ĠSh ik ha ĠBord ia , ĠC ui ying ĠYang , ĠGang ĠQian , ĠX iy u ĠM iao , ĠQian yi ĠFan , ĠTian ĠLiu , Ġand ĠStan isl av ĠSob ole vs ky Ġfor Ġdiscussions , Ġand Ġreviewers Ġfor Ġdetailed Ġfeedback .
Introduction Natural Language Processing (NLP) models are shown to capture unwanted biases and stereotypes found in the training data which raise concerns about socioeconomic, ethnic and gender discrimination when these models are deployed for public use BIBREF0 , BIBREF1 . There are numerous studies that identify algorithmic bias in NLP applications. BIBREF2 showed ethnic bias in Google autocomplete suggestions whereas BIBREF3 found gender bias in advertisement delivery systems. Additionally, BIBREF1 demonstrated that coreference resolution systems exhibit gender bias. Language modelling is a pivotal task in NLP with important downstream applications such as text generation BIBREF4 . Recent studies by BIBREF0 and BIBREF5 have shown that this task is vulnerable to gender bias in the training corpus. Two prior works focused on reducing bias in language modelling by data preprocessing BIBREF0 and word embedding debiasing BIBREF5 . In this study, we investigate the efficacy of bias reduction during training by introducing a new loss function which encourages the language model to equalize the probabilities of predicting gendered word pairs like he and she. Although we recognize that gender is non-binary, for the purpose of this study, we focus on female and male words. Our main contributions are summarized as follows: i) to our best knowledge, this study is the first one to investigate bias alleviation in text generation by direct modification of the loss function; ii) our new loss function effectively reduces gender bias in the language models during training by equalizing the probabilities of male and female words in the output; iii) we show that end-to-end debiasing of the language model can achieve word embedding debiasing; iv) we provide an interpretation of our results and draw a comparison to other existing debiasing methods. We show that our method, combined with an existing method, counterfactual data augmentation, achieves the best result and outperforms all existing methods. Related Work Recently, the study of bias in NLP applications has received increasing attention from researchers. Most relevant work in this domain can be broadly divided into two categories: word embedding debiasing and data debiasing by preprocessing. Dataset For the training data, we use Daily Mail news articles released by BIBREF9 . This dataset is composed of 219,506 articles covering a diverse range of topics including business, sports, travel, etc., and is claimed to be biased and sensational BIBREF5 . For manageability, we randomly subsample 5% of the text. The subsample has around 8.25 million tokens in total. Language Model We use a pre-trained 300-dimensional word embedding, GloVe, by BIBREF10 . We apply random search to the hyperparameter tuning of the LSTM language model. The best hyperparameters are as follows: 2 hidden layers each with 300 units, a sequence length of 35, a learning rate of 20 with an annealing schedule of decay starting from 0.25 to 0.95, a dropout rate of 0.25 and a gradient clip of 0.25. We train our models for 150 epochs, use a batch size of 48, and set early stopping with a patience of 5. Loss Function Language models are usually trained using cross-entropy loss. Cross-entropy loss at time step INLINEFORM0 is INLINEFORM1 where INLINEFORM0 is the vocabulary, INLINEFORM1 is the one hot vector of ground truth and INLINEFORM2 indicates the output softmax probability of the model. We introduce a loss term INLINEFORM0 , which aims to equalize the predicted probabilities of gender pairs such as woman and man. INLINEFORM1 INLINEFORM0 and INLINEFORM1 are a set of corresponding gender pairs, INLINEFORM2 is the size of the gender pairs set, and INLINEFORM3 indicates the output softmax probability. We use gender pairs provided by BIBREF7 . By considering only gender pairs we ensure that only gender information is neutralized and distribution over semantic concepts is not altered. For example, it will try to equalize the probabilities of congressman with congresswoman and actor with actress but distribution of congressman, congresswoman versus actor, actress will not be affected. Overall loss can be written as INLINEFORM4 where INLINEFORM0 is a hyperparameter and INLINEFORM1 is the corpus size. We observe that among the similar minima of the loss function, INLINEFORM2 encourages the model to converge towards a minimum that exhibits the lowest gender bias. Model Evaluation Language models are evaluated using perplexity, which is a standard measure of performance for unseen data. For bias evaluation, we use an array of metrics to provide a holistic diagnosis of the model behavior under debiasing treatment. These metrics are discussed in detail below. In all the evaluation metrics requiring gender pairs, we use gender pairs provided by BIBREF7 . This list contains 223 pairs, all other words are considered gender-neutral. Co-occurrence bias is computed from the model-generated texts by comparing the occurrences of all gender-neutral words with female and male words. A word is considered to be biased towards a certain gender if it occurs more frequently with words of that gender. This definition was first used by BIBREF7 and later adapted by BIBREF5 . Using the definition of gender bias similar to the one used by BIBREF5 , we define gender bias as INLINEFORM0 where INLINEFORM0 is a set of gender-neutral words, and INLINEFORM1 is the occurrences of a word INLINEFORM2 with words of gender INLINEFORM3 in the same window. This score is designed to capture unequal co-occurrences of neutral words with male and female words. Co-occurrences are computed using a sliding window of size 10 extending equally in both directions. Furthermore, we only consider words that occur more than 20 times with gendered words to exclude random effects. We also evaluate a normalized version of INLINEFORM0 which we denote by conditional co-occurrence bias, INLINEFORM1 . This is defined as INLINEFORM2 where INLINEFORM0 INLINEFORM0 is less affected by the disparity in the general distribution of male and female words in the text. The disparity between the occurrences of the two genders means that text is more inclined to mention one over the other, so it can also be considered a form of bias. We report the ratio of occurrence of male and female words in the model generated text, INLINEFORM1 , as INLINEFORM2 Another way of quantifying bias in NLP models is based on the idea of causal testing. The model is exposed to paired samples which differ only in one attribute (e.g. gender) and the disparity in the output is interpreted as bias related to that attribute. BIBREF1 and BIBREF0 applied this method to measure bias in coreference resolution and BIBREF0 also used it for evaluating gender bias in language modelling. Following the approach similar to BIBREF0 , we limit this bias evaluation to a set of gender-neutral occupations. We create a list of sentences based on a set of templates. There are two sets of templates used for evaluating causal occupation bias (Table TABREF7 ). The first set of templates is designed to measure how the probabilities of occupation words depend on the gender information in the seed. Below is an example of the first set of templates: INLINEFORM0 Here, the vertical bar separates the seed sequence that is fed into the language models from the target occupation, for which we observe the output softmax probability. We measure causal occupation bias conditioned on gender as INLINEFORM0 where INLINEFORM0 is a set of gender-neutral occupations and INLINEFORM1 is the size of the gender pairs set. For example, INLINEFORM2 is the softmax probability of the word INLINEFORM3 where the seed sequence is He is a. The second set of templates like below, aims to capture how the probabilities of gendered words depend on the occupation words in the seed. INLINEFORM4 Causal occupation bias conditioned on occupation is represented as INLINEFORM0 where INLINEFORM0 is a set of gender-neutral occupations and INLINEFORM1 is the size of the gender pairs set. For example, INLINEFORM2 is the softmax probability of man where the seed sequence is The doctor is a. We believe that both INLINEFORM0 and INLINEFORM1 contribute to gender bias in the model-generated texts. We also note that INLINEFORM2 is more easily influenced by the general disparity in male and female word probabilities. Our debiasing approach does not explicitly address the bias in the embedding layer. Therefore, we use gender-neutral occupations to measure the embedding bias to observe if debiasing the output layer also decreases the bias in the embedding. We define the embedding bias, INLINEFORM0 , as the difference between the Euclidean distance of an occupation word to male words and the distance of the occupation word to the female counterparts. This definition is equivalent to bias by projection described by BIBREF6 . We define INLINEFORM1 as INLINEFORM2 where INLINEFORM0 is a set of gender-neutral occupations, INLINEFORM1 is the size of the gender pairs set and INLINEFORM2 is the word-to-vector dictionary. Existing Approaches We apply CDA where we swap all the gendered words using a bidirectional dictionary of gender pairs described by BIBREF0 . This creates a dataset twice the size of the original data, with exactly the same contextual distributions for both genders and we use it to train the language models. We also implement the bias regularization method of BIBREF5 which debiases the word embedding during language model training by minimizing the projection of neutral words on the gender axis. We use hyperparameter tuning to find the best regularization coefficient and report results from the model trained with this coefficient. We later refer to this strategy as REG. Experiments Initially, we measure the co-occurrence bias in the training data. After training the baseline model, we implement our loss function and tune for the INLINEFORM0 hyperparameter. We test the existing debiasing approaches, CDA and REG, as well but since BIBREF5 reported that results fluctuate substantially with different REG regularization coefficients, we perform hyperparameter tuning and report the best results in Table TABREF12 . Additionally, we implement a combination of our loss function and CDA and tune for INLINEFORM1 . Finally, bias evaluation is performed for all the trained models. Causal occupation bias is measured directly from the models using template datasets discussed above and co-occurrence bias is measured from the model-generated texts, which consist of 10,000 documents of 500 words each. Results Results for the experiments are listed in Table TABREF12 . It is interesting to observe that the baseline model amplifies the bias in the training data set as measured by INLINEFORM0 and INLINEFORM1 . From measurements using the described bias metrics, our method effectively mitigates bias in language modelling without a significant increase in perplexity. At INLINEFORM2 value of 1, it reduces INLINEFORM3 by 58.95%, INLINEFORM4 by 45.74%, INLINEFORM5 by 100%, INLINEFORM6 by 98.52% and INLINEFORM7 by 98.98%. Compared to the results of CDA and REG, it achieves the best results in both occupation biases, INLINEFORM8 and INLINEFORM9 , and INLINEFORM10 . We notice that all methods result in INLINEFORM11 around 1, indicating that there are near equal amounts of female and male words in the generated texts. In our experiments we note that with increasing INLINEFORM12 , the bias steadily decreases and perplexity tends to slightly increase. This indicates that there is a trade-off between bias and perplexity. REG is not very effective in mitigating bias when compared to other methods, and fails to achieve the best result in any of the bias metrics that we used. But REG results in the best perplexity and even does better than the baseline model in this respect. This indicates that REG has a slight regularization effect. Additionally, it is interesting to note that our loss function outperforms REG in INLINEFORM0 even though REG explicitly aims to reduce gender bias in the embeddings. Although our method does not explicitly attempt geometric debiasing of the word embedding, the results show that it results in the most debiased embedding as compared to other methods. Furthermore, BIBREF8 emphasizes that geometric gender bias in word embeddings is not completely understood and existing word embedding debiasing strategies are insufficient. Our approach provides an appealing end-to-end solution for model debiasing without relying on any measure of bias in the word embedding. We believe this concept is generalizable to other NLP applications. Our method outperforms CDA in INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 . While CDA achieves slightly better results for co-occurrence biases, INLINEFORM3 and INLINEFORM4 , and results in a better perplexity. With a marginal differences, our results are comparable to those of CDA and both models seem to have similar bias mitigation effects. However, our method does not require a data augmentation step and allows training of an unbiased model directly from biased datasets. For this reason, it also requires less time to train than CDA since its training data has a smaller size without data augmentation. Furthermore, CDA fails to effectively mitigate occupation bias when compared to our approach. Although the training data for CDA does not contain gender bias, the model still exhibits some gender bias when measured with our causal occupation bias metrics. This reinforces the concept that some model-level constraints are essential to debiasing a model and dataset debiasing alone cannot be trusted. Finally, we note that the combination of CDA and our loss function outperforms all the methods in all measures of biases without compromising perplexity. Therefore, it can be argued that a cascade of these approaches can be used to optimally debias the language models. Conclusion and Discussion In this research, we propose a new approach for mitigating gender bias in neural language models and empirically show its effectiveness in reducing bias as measured with different evaluation metrics. Our research also highlights the fact that debiasing the model with bias penalties in the loss function is an effective method. We emphasize that loss function based debiasing is powerful and generalizable to other downstream NLP applications. The research also reinforces the idea that geometric debiasing of the word embedding is not a complete solution for debiasing the downstream applications but encourages end-to-end approaches to debiasing. All the debiasing techniques experimented in this paper rely on a predefined set of gender pairs in some way. CDA used gender pairs for flipping, REG uses it for gender space definition and our technique uses them for computing loss. This reliance on pre-defined set of gender pairs can be considered a limitation of these methods. It also results in another concern. There are gender associated words which do not have pairs, like pregnant. These words are not treated properly by techniques relying on gender pairs. Future work includes designing a context-aware version of our loss function which can distinguish between the unbiased and biased mentions of the gendered words and only penalize the biased version. Another interesting direction is exploring the application of this method in mitigating racial bias which brings more challenges. Acknowledgment We are grateful to Sam Bowman for helpful advice, Shikha Bordia, Cuiying Yang, Gang Qian, Xiyu Miao, Qianyi Fan, Tian Liu, and Stanislav Sobolevsky for discussions, and reviewers for detailed feedback.
how is mitigation of gender bias evaluated?
5f60defb546f35d25a094ff34781cddd4119e400
how Ġis Ġmitigation Ġof Ġgender Ġbias Ġevaluated ?
Using ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1
<s> how Ġis Ġmitigation Ġof Ġgender Ġbias Ġevaluated ? </s> Introduction </s> Natural ĠLanguage ĠProcessing Ġ( N LP ) Ġmodels Ġare Ġshown Ġto Ġcapture Ġunwanted Ġbiases Ġand Ġstereotypes Ġfound Ġin Ġthe Ġtraining Ġdata Ġwhich Ġraise Ġconcerns Ġabout Ġsocioeconomic , Ġethnic Ġand Ġgender Ġdiscrimination Ġwhen Ġthese Ġmodels Ġare Ġdeployed Ġfor Ġpublic Ġuse ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ. </s> There Ġare Ġnumerous Ġstudies Ġthat Ġidentify Ġalgorith mic Ġbias Ġin ĠN LP Ġapplications . ĠB IB REF 2 Ġshowed Ġethnic Ġbias Ġin ĠGoogle Ġaut ocom plete Ġsuggestions Ġwhereas ĠB IB REF 3 Ġfound Ġgender Ġbias Ġin Ġadvertisement Ġdelivery Ġsystems . ĠAdditionally , ĠB IB REF 1 Ġdemonstrated Ġthat Ġcore ference Ġresolution Ġsystems Ġexhibit Ġgender Ġbias . </s> Language Ġmodelling Ġis Ġa Ġpivotal Ġtask Ġin ĠN LP Ġwith Ġimportant Ġdownstream Ġapplications Ġsuch Ġas Ġtext Ġgeneration ĠB IB REF 4 Ġ. ĠRecent Ġstudies Ġby ĠB IB REF 0 Ġand ĠB IB REF 5 Ġhave Ġshown Ġthat Ġthis Ġtask Ġis Ġvulnerable Ġto Ġgender Ġbias Ġin Ġthe Ġtraining Ġcorpus . ĠTwo Ġprior Ġworks Ġfocused Ġon Ġreducing Ġbias Ġin Ġlanguage Ġmodelling Ġby Ġdata Ġpre processing ĠB IB REF 0 Ġand Ġword Ġembed ding Ġdeb i asing ĠB IB REF 5 Ġ. ĠIn Ġthis Ġstudy , Ġwe Ġinvestigate Ġthe Ġefficacy Ġof Ġbias Ġreduction Ġduring Ġtraining Ġby Ġintroducing Ġa Ġnew Ġloss Ġfunction Ġwhich Ġencourages Ġthe Ġlanguage Ġmodel Ġto Ġequal ize Ġthe Ġprobabilities Ġof Ġpredicting Ġg endered Ġword Ġpairs Ġlike Ġhe Ġand Ġshe . ĠAlthough Ġwe Ġrecognize Ġthat Ġgender Ġis Ġnon - binary , Ġfor Ġthe Ġpurpose Ġof Ġthis Ġstudy , Ġwe Ġfocus Ġon Ġfemale Ġand Ġmale Ġwords . </s> Our Ġmain Ġcontributions Ġare Ġsummarized Ġas Ġfollows : Ġi ) Ġto Ġour Ġbest Ġknowledge , Ġthis Ġstudy Ġis Ġthe Ġfirst Ġone Ġto Ġinvestigate Ġbias Ġallev iation Ġin Ġtext Ġgeneration Ġby Ġdirect Ġmodification Ġof Ġthe Ġloss Ġfunction ; Ġii ) Ġour Ġnew Ġloss Ġfunction Ġeffectively Ġreduces Ġgender Ġbias Ġin Ġthe Ġlanguage Ġmodels Ġduring Ġtraining Ġby Ġequal izing Ġthe Ġprobabilities Ġof Ġmale Ġand Ġfemale Ġwords Ġin Ġthe Ġoutput ; Ġiii ) Ġwe Ġshow Ġthat Ġend - to - end Ġdeb i asing Ġof Ġthe Ġlanguage Ġmodel Ġcan Ġachieve Ġword Ġembed ding Ġdeb i asing ; Ġiv ) Ġwe Ġprovide Ġan Ġinterpretation Ġof Ġour Ġresults Ġand Ġdraw Ġa Ġcomparison Ġto Ġother Ġexisting Ġdeb i asing Ġmethods . ĠWe Ġshow Ġthat Ġour Ġmethod , Ġcombined Ġwith Ġan Ġexisting Ġmethod , Ġcounter fact ual Ġdata Ġaug mentation , Ġachieves Ġthe Ġbest Ġresult Ġand Ġoutper forms Ġall Ġexisting Ġmethods . </s> Related ĠWork </s> Recently , Ġthe Ġstudy Ġof Ġbias Ġin ĠN LP Ġapplications Ġhas Ġreceived Ġincreasing Ġattention Ġfrom Ġresearchers . ĠMost Ġrelevant Ġwork Ġin Ġthis Ġdomain Ġcan Ġbe Ġbroadly Ġdivided Ġinto Ġtwo Ġcategories : Ġword Ġembed ding Ġdeb i asing Ġand Ġdata Ġdeb i asing Ġby Ġpre processing . </s> Dat as et </s> For Ġthe Ġtraining Ġdata , Ġwe Ġuse ĠDaily ĠMail Ġnews Ġarticles Ġreleased Ġby ĠB IB REF 9 Ġ. ĠThis Ġdataset Ġis Ġcomposed Ġof Ġ219 , 506 Ġarticles Ġcovering Ġa Ġdiverse Ġrange Ġof Ġtopics Ġincluding Ġbusiness , Ġsports , Ġtravel , Ġetc ., Ġand Ġis Ġclaimed Ġto Ġbe Ġbiased Ġand Ġsensational ĠB IB REF 5 Ġ. ĠFor Ġmanage ability , Ġwe Ġrandomly Ġsubs ample Ġ5 % Ġof Ġthe Ġtext . ĠThe Ġsubs ample Ġhas Ġaround Ġ8 . 25 Ġmillion Ġtokens Ġin Ġtotal . </s> Language ĠModel </s> We Ġuse Ġa Ġpre - trained Ġ300 - dimensional Ġword Ġembed ding , ĠGl o Ve , Ġby ĠB IB REF 10 Ġ. ĠWe Ġapply Ġrandom Ġsearch Ġto Ġthe Ġhyper param eter Ġtuning Ġof Ġthe ĠL ST M Ġlanguage Ġmodel . ĠThe Ġbest Ġhyper param eters Ġare Ġas Ġfollows : Ġ2 Ġhidden Ġlayers Ġeach Ġwith Ġ300 Ġunits , Ġa Ġsequence Ġlength Ġof Ġ35 , Ġa Ġlearning Ġrate Ġof Ġ20 Ġwith Ġan Ġan ne aling Ġschedule Ġof Ġdecay Ġstarting Ġfrom Ġ0 . 25 Ġto Ġ0 . 95 , Ġa Ġdrop out Ġrate Ġof Ġ0 . 25 Ġand Ġa Ġgradient Ġclip Ġof Ġ0 . 25 . ĠWe Ġtrain Ġour Ġmodels Ġfor Ġ150 Ġepoch s , Ġuse Ġa Ġbatch Ġsize Ġof Ġ48 , Ġand Ġset Ġearly Ġstopping Ġwith Ġa Ġpatience Ġof Ġ5 . </s> L oss ĠFunction </s> Language Ġmodels Ġare Ġusually Ġtrained Ġusing Ġcross - ent ropy Ġloss . ĠCross - ent ropy Ġloss Ġat Ġtime Ġstep ĠIN LINE FORM 0 Ġis ĠIN LINE FORM 1 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġvocabulary , ĠIN LINE FORM 1 Ġis Ġthe Ġone Ġhot Ġvector Ġof Ġground Ġtruth Ġand ĠIN LINE FORM 2 Ġindicates Ġthe Ġoutput Ġsoft max Ġprobability Ġof Ġthe Ġmodel . </s> We Ġintroduce Ġa Ġloss Ġterm ĠIN LINE FORM 0 Ġ, Ġwhich Ġaims Ġto Ġequal ize Ġthe Ġpredicted Ġprobabilities Ġof Ġgender Ġpairs Ġsuch Ġas Ġwoman Ġand Ġman . ĠIN LINE FORM 1 </s> IN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare Ġa Ġset Ġof Ġcorresponding Ġgender Ġpairs , ĠIN LINE FORM 2 Ġis Ġthe Ġsize Ġof Ġthe Ġgender Ġpairs Ġset , Ġand ĠIN LINE FORM 3 Ġindicates Ġthe Ġoutput Ġsoft max Ġprobability . ĠWe Ġuse Ġgender Ġpairs Ġprovided Ġby ĠB IB REF 7 Ġ. ĠBy Ġconsidering Ġonly Ġgender Ġpairs Ġwe Ġensure Ġthat Ġonly Ġgender Ġinformation Ġis Ġneutral ized Ġand Ġdistribution Ġover Ġsemantic Ġconcepts Ġis Ġnot Ġaltered . ĠFor Ġexample , Ġit Ġwill Ġtry Ġto Ġequal ize Ġthe Ġprobabilities Ġof Ġcongressman Ġwith Ġcongress woman Ġand Ġactor Ġwith Ġactress Ġbut Ġdistribution Ġof Ġcongressman , Ġcongress woman Ġversus Ġactor , Ġactress Ġwill Ġnot Ġbe Ġaffected . ĠOverall Ġloss Ġcan Ġbe Ġwritten Ġas ĠIN LINE FORM 4 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġhyper param eter Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġcorpus Ġsize . ĠWe Ġobserve Ġthat Ġamong Ġthe Ġsimilar Ġmin ima Ġof Ġthe Ġloss Ġfunction , ĠIN LINE FORM 2 Ġencourages Ġthe Ġmodel Ġto Ġconverge Ġtowards Ġa Ġminimum Ġthat Ġexhibits Ġthe Ġlowest Ġgender Ġbias . </s> Model ĠEvaluation </s> Language Ġmodels Ġare Ġevaluated Ġusing Ġperplex ity , Ġwhich Ġis Ġa Ġstandard Ġmeasure Ġof Ġperformance Ġfor Ġunseen Ġdata . ĠFor Ġbias Ġevaluation , Ġwe Ġuse Ġan Ġarray Ġof Ġmetrics Ġto Ġprovide Ġa Ġholistic Ġdiagnosis Ġof Ġthe Ġmodel Ġbehavior Ġunder Ġdeb i asing Ġtreatment . ĠThese Ġmetrics Ġare Ġdiscussed Ġin Ġdetail Ġbelow . ĠIn Ġall Ġthe Ġevaluation Ġmetrics Ġrequiring Ġgender Ġpairs , Ġwe Ġuse Ġgender Ġpairs Ġprovided Ġby ĠB IB REF 7 Ġ. ĠThis Ġlist Ġcontains Ġ223 Ġpairs , Ġall Ġother Ġwords Ġare Ġconsidered Ġgender - neutral . </s> Co - occ urrence Ġbias Ġis Ġcomputed Ġfrom Ġthe Ġmodel - generated Ġtexts Ġby Ġcomparing Ġthe Ġoccurrences Ġof Ġall Ġgender - neutral Ġwords Ġwith Ġfemale Ġand Ġmale Ġwords . ĠA Ġword Ġis Ġconsidered Ġto Ġbe Ġbiased Ġtowards Ġa Ġcertain Ġgender Ġif Ġit Ġoccurs Ġmore Ġfrequently Ġwith Ġwords Ġof Ġthat Ġgender . ĠThis Ġdefinition Ġwas Ġfirst Ġused Ġby ĠB IB REF 7 Ġand Ġlater Ġadapted Ġby ĠB IB REF 5 Ġ. ĠUsing Ġthe Ġdefinition Ġof Ġgender Ġbias Ġsimilar Ġto Ġthe Ġone Ġused Ġby ĠB IB REF 5 Ġ, Ġwe Ġdefine Ġgender Ġbias Ġas ĠIN LINE FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġset Ġof Ġgender - neutral Ġwords , Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġoccurrences Ġof Ġa Ġword ĠIN LINE FORM 2 Ġwith Ġwords Ġof Ġgender ĠIN LINE FORM 3 Ġin Ġthe Ġsame Ġwindow . ĠThis Ġscore Ġis Ġdesigned Ġto Ġcapture Ġunequal Ġco - occ ur rences Ġof Ġneutral Ġwords Ġwith Ġmale Ġand Ġfemale Ġwords . ĠCo - occ ur rences Ġare Ġcomputed Ġusing Ġa Ġsliding Ġwindow Ġof Ġsize Ġ10 Ġextending Ġequally Ġin Ġboth Ġdirections . ĠFurthermore , Ġwe Ġonly Ġconsider Ġwords Ġthat Ġoccur Ġmore Ġthan Ġ20 Ġtimes Ġwith Ġg endered Ġwords Ġto Ġexclude Ġrandom Ġeffects . </s> We Ġalso Ġevaluate Ġa Ġnormalized Ġversion Ġof ĠIN LINE FORM 0 Ġwhich Ġwe Ġdenote Ġby Ġconditional Ġco - occ urrence Ġbias , ĠIN LINE FORM 1 Ġ. ĠThis Ġis Ġdefined Ġas ĠIN LINE FORM 2 </s> where ĠIN LINE FORM 0 </s> IN LINE FORM 0 Ġis Ġless Ġaffected Ġby Ġthe Ġdisparity Ġin Ġthe Ġgeneral Ġdistribution Ġof Ġmale Ġand Ġfemale Ġwords Ġin Ġthe Ġtext . ĠThe Ġdisparity Ġbetween Ġthe Ġoccurrences Ġof Ġthe Ġtwo Ġgenders Ġmeans Ġthat Ġtext Ġis Ġmore Ġinclined Ġto Ġmention Ġone Ġover Ġthe Ġother , Ġso Ġit Ġcan Ġalso Ġbe Ġconsidered Ġa Ġform Ġof Ġbias . ĠWe Ġreport Ġthe Ġratio Ġof Ġoccurrence Ġof Ġmale Ġand Ġfemale Ġwords Ġin Ġthe Ġmodel Ġgenerated Ġtext , ĠIN LINE FORM 1 Ġ, Ġas ĠIN LINE FORM 2 </s> Another Ġway Ġof Ġquant ifying Ġbias Ġin ĠN LP Ġmodels Ġis Ġbased Ġon Ġthe Ġidea Ġof Ġcausal Ġtesting . ĠThe Ġmodel Ġis Ġexposed Ġto Ġpaired Ġsamples Ġwhich Ġdiffer Ġonly Ġin Ġone Ġattribute Ġ( e . g . Ġgender ) Ġand Ġthe Ġdisparity Ġin Ġthe Ġoutput Ġis Ġinterpreted Ġas Ġbias Ġrelated Ġto Ġthat Ġattribute . ĠB IB REF 1 Ġand ĠB IB REF 0 Ġapplied Ġthis Ġmethod Ġto Ġmeasure Ġbias Ġin Ġcore ference Ġresolution Ġand ĠB IB REF 0 Ġalso Ġused Ġit Ġfor Ġevaluating Ġgender Ġbias Ġin Ġlanguage Ġmodelling . </s> Following Ġthe Ġapproach Ġsimilar Ġto ĠB IB REF 0 Ġ, Ġwe Ġlimit Ġthis Ġbias Ġevaluation Ġto Ġa Ġset Ġof Ġgender - neutral Ġoccupations . ĠWe Ġcreate Ġa Ġlist Ġof Ġsentences Ġbased Ġon Ġa Ġset Ġof Ġtemplates . ĠThere Ġare Ġtwo Ġsets Ġof Ġtemplates Ġused Ġfor Ġevaluating Ġcausal Ġoccupation Ġbias Ġ( Table ĠT AB REF 7 Ġ). ĠThe Ġfirst Ġset Ġof Ġtemplates Ġis Ġdesigned Ġto Ġmeasure Ġhow Ġthe Ġprobabilities Ġof Ġoccupation Ġwords Ġdepend Ġon Ġthe Ġgender Ġinformation Ġin Ġthe Ġseed . ĠBelow Ġis Ġan Ġexample Ġof Ġthe Ġfirst Ġset Ġof Ġtemplates : ĠIN LINE FORM 0 </s> Here , Ġthe Ġvertical Ġbar Ġseparates Ġthe Ġseed Ġsequence Ġthat Ġis Ġfed Ġinto Ġthe Ġlanguage Ġmodels Ġfrom Ġthe Ġtarget Ġoccupation , Ġfor Ġwhich Ġwe Ġobserve Ġthe Ġoutput Ġsoft max Ġprobability . ĠWe Ġmeasure Ġcausal Ġoccupation Ġbias Ġconditioned Ġon Ġgender Ġas ĠIN LINE FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġset Ġof Ġgender - neutral Ġoccupations Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġsize Ġof Ġthe Ġgender Ġpairs Ġset . ĠFor Ġexample , ĠIN LINE FORM 2 Ġis Ġthe Ġsoft max Ġprobability Ġof Ġthe Ġword ĠIN LINE FORM 3 Ġwhere Ġthe Ġseed Ġsequence Ġis ĠHe Ġis Ġa . ĠThe Ġsecond Ġset Ġof Ġtemplates Ġlike Ġbelow , Ġaims Ġto Ġcapture Ġhow Ġthe Ġprobabilities Ġof Ġg endered Ġwords Ġdepend Ġon Ġthe Ġoccupation Ġwords Ġin Ġthe Ġseed . ĠIN LINE FORM 4 </s> Ca usal Ġoccupation Ġbias Ġconditioned Ġon Ġoccupation Ġis Ġrepresented Ġas ĠIN LINE FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġset Ġof Ġgender - neutral Ġoccupations Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġsize Ġof Ġthe Ġgender Ġpairs Ġset . ĠFor Ġexample , ĠIN LINE FORM 2 Ġis Ġthe Ġsoft max Ġprobability Ġof Ġman Ġwhere Ġthe Ġseed Ġsequence Ġis ĠThe Ġdoctor Ġis Ġa . </s> We Ġbelieve Ġthat Ġboth ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġcontribute Ġto Ġgender Ġbias Ġin Ġthe Ġmodel - generated Ġtexts . ĠWe Ġalso Ġnote Ġthat ĠIN LINE FORM 2 Ġis Ġmore Ġeasily Ġinfluenced Ġby Ġthe Ġgeneral Ġdisparity Ġin Ġmale Ġand Ġfemale Ġword Ġprobabilities . </s> Our Ġdeb i asing Ġapproach Ġdoes Ġnot Ġexplicitly Ġaddress Ġthe Ġbias Ġin Ġthe Ġembed ding Ġlayer . ĠTherefore , Ġwe Ġuse Ġgender - neutral Ġoccupations Ġto Ġmeasure Ġthe Ġembed ding Ġbias Ġto Ġobserve Ġif Ġdeb i asing Ġthe Ġoutput Ġlayer Ġalso Ġdecreases Ġthe Ġbias Ġin Ġthe Ġembed ding . ĠWe Ġdefine Ġthe Ġembed ding Ġbias , ĠIN LINE FORM 0 Ġ, Ġas Ġthe Ġdifference Ġbetween Ġthe ĠEucl ide an Ġdistance Ġof Ġan Ġoccupation Ġword Ġto Ġmale Ġwords Ġand Ġthe Ġdistance Ġof Ġthe Ġoccupation Ġword Ġto Ġthe Ġfemale Ġcounterparts . ĠThis Ġdefinition Ġis Ġequivalent Ġto Ġbias Ġby Ġprojection Ġdescribed Ġby ĠB IB REF 6 Ġ. ĠWe Ġdefine ĠIN LINE FORM 1 Ġas ĠIN LINE FORM 2 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġset Ġof Ġgender - neutral Ġoccupations , ĠIN LINE FORM 1 Ġis Ġthe Ġsize Ġof Ġthe Ġgender Ġpairs Ġset Ġand ĠIN LINE FORM 2 Ġis Ġthe Ġword - to - vector Ġdictionary . </s> Ex isting ĠAppro aches </s> We Ġapply ĠC DA Ġwhere Ġwe Ġswap Ġall Ġthe Ġg endered Ġwords Ġusing Ġa Ġbid irection al Ġdictionary Ġof Ġgender Ġpairs Ġdescribed Ġby ĠB IB REF 0 Ġ. ĠThis Ġcreates Ġa Ġdataset Ġtwice Ġthe Ġsize Ġof Ġthe Ġoriginal Ġdata , Ġwith Ġexactly Ġthe Ġsame Ġcontextual Ġdistributions Ġfor Ġboth Ġgenders Ġand Ġwe Ġuse Ġit Ġto Ġtrain Ġthe Ġlanguage Ġmodels . </s> We Ġalso Ġimplement Ġthe Ġbias Ġregular ization Ġmethod Ġof ĠB IB REF 5 Ġwhich Ġdeb i ases Ġthe Ġword Ġembed ding Ġduring Ġlanguage Ġmodel Ġtraining Ġby Ġminimizing Ġthe Ġprojection Ġof Ġneutral Ġwords Ġon Ġthe Ġgender Ġaxis . ĠWe Ġuse Ġhyper param eter Ġtuning Ġto Ġfind Ġthe Ġbest Ġregular ization Ġcoefficient Ġand Ġreport Ġresults Ġfrom Ġthe Ġmodel Ġtrained Ġwith Ġthis Ġcoefficient . ĠWe Ġlater Ġrefer Ġto Ġthis Ġstrategy Ġas ĠREG . </s> Exper iments </s> Initially , Ġwe Ġmeasure Ġthe Ġco - occ urrence Ġbias Ġin Ġthe Ġtraining Ġdata . ĠAfter Ġtraining Ġthe Ġbaseline Ġmodel , Ġwe Ġimplement Ġour Ġloss Ġfunction Ġand Ġtune Ġfor Ġthe ĠIN LINE FORM 0 Ġhyper param eter . ĠWe Ġtest Ġthe Ġexisting Ġdeb i asing Ġapproaches , ĠC DA Ġand ĠREG , Ġas Ġwell Ġbut Ġsince ĠB IB REF 5 Ġreported Ġthat Ġresults Ġfluct uate Ġsubstantially Ġwith Ġdifferent ĠREG Ġregular ization Ġcoefficients , Ġwe Ġperform Ġhyper param eter Ġtuning Ġand Ġreport Ġthe Ġbest Ġresults Ġin ĠTable ĠT AB REF 12 Ġ. ĠAdditionally , Ġwe Ġimplement Ġa Ġcombination Ġof Ġour Ġloss Ġfunction Ġand ĠC DA Ġand Ġtune Ġfor ĠIN LINE FORM 1 Ġ. ĠFinally , Ġbias Ġevaluation Ġis Ġperformed Ġfor Ġall Ġthe Ġtrained Ġmodels . ĠCa usal Ġoccupation Ġbias Ġis Ġmeasured Ġdirectly Ġfrom Ġthe Ġmodels Ġusing Ġtemplate Ġdatasets Ġdiscussed Ġabove Ġand Ġco - occ urrence Ġbias Ġis Ġmeasured Ġfrom Ġthe Ġmodel - generated Ġtexts , Ġwhich Ġconsist Ġof Ġ10 , 000 Ġdocuments Ġof Ġ500 Ġwords Ġeach . </s> Results </s> Results Ġfor Ġthe Ġexperiments Ġare Ġlisted Ġin ĠTable ĠT AB REF 12 Ġ. ĠIt Ġis Ġinteresting Ġto Ġobserve Ġthat Ġthe Ġbaseline Ġmodel Ġampl ifies Ġthe Ġbias Ġin Ġthe Ġtraining Ġdata Ġset Ġas Ġmeasured Ġby ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġ. ĠFrom Ġmeasurements Ġusing Ġthe Ġdescribed Ġbias Ġmetrics , Ġour Ġmethod Ġeffectively Ġmit ig ates Ġbias Ġin Ġlanguage Ġmodelling Ġwithout Ġa Ġsignificant Ġincrease Ġin Ġperplex ity . ĠAt ĠIN LINE FORM 2 Ġvalue Ġof Ġ1 , Ġit Ġreduces ĠIN LINE FORM 3 Ġby Ġ58 . 95 %, ĠIN LINE FORM 4 Ġby Ġ45 . 74 %, ĠIN LINE FORM 5 Ġby Ġ100 %, ĠIN LINE FORM 6 Ġby Ġ98 . 52 % Ġand ĠIN LINE FORM 7 Ġby Ġ98 . 98 %. ĠCompared Ġto Ġthe Ġresults Ġof ĠC DA Ġand ĠREG , Ġit Ġachieves Ġthe Ġbest Ġresults Ġin Ġboth Ġoccupation Ġbiases , ĠIN LINE FORM 8 Ġand ĠIN LINE FORM 9 Ġ, Ġand ĠIN LINE FORM 10 Ġ. ĠWe Ġnotice Ġthat Ġall Ġmethods Ġresult Ġin ĠIN LINE FORM 11 Ġaround Ġ1 , Ġindicating Ġthat Ġthere Ġare Ġnear Ġequal Ġamounts Ġof Ġfemale Ġand Ġmale Ġwords Ġin Ġthe Ġgenerated Ġtexts . ĠIn Ġour Ġexperiments Ġwe Ġnote Ġthat Ġwith Ġincreasing ĠIN LINE FORM 12 Ġ, Ġthe Ġbias Ġsteadily Ġdecreases Ġand Ġperplex ity Ġtends Ġto Ġslightly Ġincrease . ĠThis Ġindicates Ġthat Ġthere Ġis Ġa Ġtrade - off Ġbetween Ġbias Ġand Ġperplex ity . </s> REG Ġis Ġnot Ġvery Ġeffective Ġin Ġmitigating Ġbias Ġwhen Ġcompared Ġto Ġother Ġmethods , Ġand Ġfails Ġto Ġachieve Ġthe Ġbest Ġresult Ġin Ġany Ġof Ġthe Ġbias Ġmetrics Ġthat Ġwe Ġused . ĠBut ĠREG Ġresults Ġin Ġthe Ġbest Ġperplex ity Ġand Ġeven Ġdoes Ġbetter Ġthan Ġthe Ġbaseline Ġmodel Ġin Ġthis Ġrespect . ĠThis Ġindicates Ġthat ĠREG Ġhas Ġa Ġslight Ġregular ization Ġeffect . ĠAdditionally , Ġit Ġis Ġinteresting Ġto Ġnote Ġthat Ġour Ġloss Ġfunction Ġoutper forms ĠREG Ġin ĠIN LINE FORM 0 Ġeven Ġthough ĠREG Ġexplicitly Ġaims Ġto Ġreduce Ġgender Ġbias Ġin Ġthe Ġembed d ings . ĠAlthough Ġour Ġmethod Ġdoes Ġnot Ġexplicitly Ġattempt Ġgeometric Ġdeb i asing Ġof Ġthe Ġword Ġembed ding , Ġthe Ġresults Ġshow Ġthat Ġit Ġresults Ġin Ġthe Ġmost Ġdeb i ased Ġembed ding Ġas Ġcompared Ġto Ġother Ġmethods . ĠFurthermore , ĠB IB REF 8 Ġemphasizes Ġthat Ġgeometric Ġgender Ġbias Ġin Ġword Ġembed d ings Ġis Ġnot Ġcompletely Ġunderstood Ġand Ġexisting Ġword Ġembed ding Ġdeb i asing Ġstrategies Ġare Ġinsufficient . ĠOur Ġapproach Ġprovides Ġan Ġappealing Ġend - to - end Ġsolution Ġfor Ġmodel Ġdeb i asing Ġwithout Ġrelying Ġon Ġany Ġmeasure Ġof Ġbias Ġin Ġthe Ġword Ġembed ding . ĠWe Ġbelieve Ġthis Ġconcept Ġis Ġgeneral izable Ġto Ġother ĠN LP Ġapplications . </s> Our Ġmethod Ġoutper forms ĠC DA Ġin ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ, Ġand ĠIN LINE FORM 2 Ġ. ĠWhile ĠC DA Ġachieves Ġslightly Ġbetter Ġresults Ġfor Ġco - occ urrence Ġbiases , ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġ, Ġand Ġresults Ġin Ġa Ġbetter Ġperplex ity . ĠWith Ġa Ġmarginal Ġdifferences , Ġour Ġresults Ġare Ġcomparable Ġto Ġthose Ġof ĠC DA Ġand Ġboth Ġmodels Ġseem Ġto Ġhave Ġsimilar Ġbias Ġmitigation Ġeffects . ĠHowever , Ġour Ġmethod Ġdoes Ġnot Ġrequire Ġa Ġdata Ġaug mentation Ġstep Ġand Ġallows Ġtraining Ġof Ġan Ġunbiased Ġmodel Ġdirectly Ġfrom Ġbiased Ġdatasets . ĠFor Ġthis Ġreason , Ġit Ġalso Ġrequires Ġless Ġtime Ġto Ġtrain Ġthan ĠC DA Ġsince Ġits Ġtraining Ġdata Ġhas Ġa Ġsmaller Ġsize Ġwithout Ġdata Ġaug mentation . ĠFurthermore , ĠC DA Ġfails Ġto Ġeffectively Ġmitigate Ġoccupation Ġbias Ġwhen Ġcompared Ġto Ġour Ġapproach . ĠAlthough Ġthe Ġtraining Ġdata Ġfor ĠC DA Ġdoes Ġnot Ġcontain Ġgender Ġbias , Ġthe Ġmodel Ġstill Ġexhibits Ġsome Ġgender Ġbias Ġwhen Ġmeasured Ġwith Ġour Ġcausal Ġoccupation Ġbias Ġmetrics . ĠThis Ġreinforces Ġthe Ġconcept Ġthat Ġsome Ġmodel - level Ġconstraints Ġare Ġessential Ġto Ġdeb i asing Ġa Ġmodel Ġand Ġdataset Ġdeb i asing Ġalone Ġcannot Ġbe Ġtrusted . </s> Finally , Ġwe Ġnote Ġthat Ġthe Ġcombination Ġof ĠC DA Ġand Ġour Ġloss Ġfunction Ġoutper forms Ġall Ġthe Ġmethods Ġin Ġall Ġmeasures Ġof Ġbiases Ġwithout Ġcompromising Ġperplex ity . ĠTherefore , Ġit Ġcan Ġbe Ġargued Ġthat Ġa Ġcascade Ġof Ġthese Ġapproaches Ġcan Ġbe Ġused Ġto Ġoptim ally Ġdeb ias Ġthe Ġlanguage Ġmodels . </s> Conclusion Ġand ĠDiscussion </s> In Ġthis Ġresearch , Ġwe Ġpropose Ġa Ġnew Ġapproach Ġfor Ġmitigating Ġgender Ġbias Ġin Ġneural Ġlanguage Ġmodels Ġand Ġempir ically Ġshow Ġits Ġeffectiveness Ġin Ġreducing Ġbias Ġas Ġmeasured Ġwith Ġdifferent Ġevaluation Ġmetrics . ĠOur Ġresearch Ġalso Ġhighlights Ġthe Ġfact Ġthat Ġdeb i asing Ġthe Ġmodel Ġwith Ġbias Ġpenalties Ġin Ġthe Ġloss Ġfunction Ġis Ġan Ġeffective Ġmethod . ĠWe Ġemphasize Ġthat Ġloss Ġfunction Ġbased Ġdeb i asing Ġis Ġpowerful Ġand Ġgeneral izable Ġto Ġother Ġdownstream ĠN LP Ġapplications . ĠThe Ġresearch Ġalso Ġreinforces Ġthe Ġidea Ġthat Ġgeometric Ġdeb i asing Ġof Ġthe Ġword Ġembed ding Ġis Ġnot Ġa Ġcomplete Ġsolution Ġfor Ġdeb i asing Ġthe Ġdownstream Ġapplications Ġbut Ġencourages Ġend - to - end Ġapproaches Ġto Ġdeb i asing . </s> All Ġthe Ġdeb i asing Ġtechniques Ġexperimented Ġin Ġthis Ġpaper Ġrely Ġon Ġa Ġpred efined Ġset Ġof Ġgender Ġpairs Ġin Ġsome Ġway . ĠC DA Ġused Ġgender Ġpairs Ġfor Ġflipping , ĠREG Ġuses Ġit Ġfor Ġgender Ġspace Ġdefinition Ġand Ġour Ġtechnique Ġuses Ġthem Ġfor Ġcomputing Ġloss . ĠThis Ġreliance Ġon Ġpre - defined Ġset Ġof Ġgender Ġpairs Ġcan Ġbe Ġconsidered Ġa Ġlimitation Ġof Ġthese Ġmethods . ĠIt Ġalso Ġresults Ġin Ġanother Ġconcern . ĠThere Ġare Ġgender Ġassociated Ġwords Ġwhich Ġdo Ġnot Ġhave Ġpairs , Ġlike Ġpregnant . ĠThese Ġwords Ġare Ġnot Ġtreated Ġproperly Ġby Ġtechniques Ġrelying Ġon Ġgender Ġpairs . </s> Future Ġwork Ġincludes Ġdesigning Ġa Ġcontext - aware Ġversion Ġof Ġour Ġloss Ġfunction Ġwhich Ġcan Ġdistinguish Ġbetween Ġthe Ġunbiased Ġand Ġbiased Ġmentions Ġof Ġthe Ġg endered Ġwords Ġand Ġonly Ġpenal ize Ġthe Ġbiased Ġversion . ĠAnother Ġinteresting Ġdirection Ġis Ġexploring Ġthe Ġapplication Ġof Ġthis Ġmethod Ġin Ġmitigating Ġracial Ġbias Ġwhich Ġbrings Ġmore Ġchallenges . </s> Acknowled gment </s> We Ġare Ġgrateful Ġto ĠSam ĠBowman Ġfor Ġhelpful Ġadvice , ĠSh ik ha ĠBord ia , ĠC ui ying ĠYang , ĠGang ĠQian , ĠX iy u ĠM iao , ĠQian yi ĠFan , ĠTian ĠLiu , Ġand ĠStan isl av ĠSob ole vs ky Ġfor Ġdiscussions , Ġand Ġreviewers Ġfor Ġdetailed Ġfeedback .