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Introduction </s> - Ġ! ` Soc or ro , Ġme Ġha Ġpic ado Ġun a Ġv ÃŃ bor a ! </s> - Ġ? ` C ob ra ? </s> - ĠNo , Ġgrat is .[ 5 ] </s> Google ĠTranslation : </s> - ĠHelp , ĠI Ġwas Ġbitten Ġby Ġa Ġsnake ! </s> - ĠDoes Ġit Ġcharge ? </s> - ĠNot Ġfree . </s> [ 4 ] https :// github . com / b far zin / h aha _ 2019 _ final , ĠAccessed Ġon Ġ19 ĠJune Ġ2019 Ġ[ 5 ] https :// www . f luent in 3 months . com / sp anish - j okes /, ĠAccessed Ġon Ġ19 ĠJune Ġ2019 </s> Hum or Ġdoes Ġnot Ġtranslate Ġwell Ġbecause Ġit Ġoften Ġrelies Ġon Ġdouble - meaning Ġor Ġa Ġsubtle Ġplay Ġon Ġword Ġchoice , Ġpronunciation , Ġor Ġcontext . ĠThese Ġissues Ġare Ġfurther Ġexacerbated Ġin Ġareas Ġwhere Ġspace Ġis Ġa Ġpremium Ġ( as Ġfrequent Ġon Ġsocial Ġmedia Ġplatforms ), Ġoften Ġleading Ġto Ġusage Ġand Ġdevelopment Ġof Ġshorthand , Ġin - j okes , Ġand Ġself - reference . ĠThus , Ġbuilding Ġa Ġsystem Ġto Ġclassify Ġthe Ġhumor Ġof Ġtweets Ġis Ġa Ġdifficult Ġtask . ĠHowever , Ġwith Ġtransfer - learning Ġand Ġthe ĠFast . ai Ġlibrary , Ġwe Ġcan Ġbuild Ġa Ġhigh Ġquality Ġclass ifier Ġin Ġa Ġforeign Ġlanguage . ĠOur Ġsystem Ġoutper forms Ġa ĠNa ive ĠBay es ĠSupport ĠVector ĠMachine Ġ( N BS VM ) Ġbaseline , Ġwhich Ġis Ġfrequently Ġconsidered Ġa Ġ" strong Ġbaseline " Ġfor Ġmany ĠNatural ĠLanguage ĠProcessing Ġ( N LP ) Ġrelated Ġtasks Ġ( see ĠWang Ġet Ġal ĠB IB REF 0 Ġ). </s> Rather Ġthan Ġhand - crafted Ġlanguage Ġfeatures , Ġwe Ġhave Ġtaken Ġan Ġ" end Ġto Ġend " Ġapproach Ġbuilding Ġfrom Ġthe Ġraw Ġtext Ġto Ġa Ġfinal Ġmodel Ġthat Ġachieves Ġthe Ġtasks Ġas Ġpresented . ĠOur Ġpaper Ġlays Ġout Ġthe Ġdetails Ġof Ġthe Ġsystem Ġand Ġour Ġcode Ġcan Ġbe Ġfound Ġin Ġa ĠGitHub Ġrepository Ġfor Ġuse Ġby Ġother Ġresearchers Ġto Ġextend Ġthe Ġstate Ġof Ġthe Ġart Ġin Ġsentiment Ġanalysis . </s> Task Ġand ĠDat as et ĠDescription </s> The ĠHum or ĠAnalysis Ġbased Ġon ĠHuman ĠAn notation Ġ( HAHA ) Ġ2019 ĠB IB REF 1 Ġcompetition Ġasked Ġfor Ġanalysis Ġof Ġtwo Ġtasks Ġin Ġthe ĠSpanish Ġlanguage Ġbased Ġon Ġa Ġcorpus Ġof Ġpublicly Ġcollected Ġdata Ġdescribed Ġin ĠCastro Ġet Ġal . ĠB IB REF 2 Ġ: </s> The ĠHA HA Ġdataset Ġincludes Ġlabeled Ġdata Ġfor Ġ24 , 000 Ġtweets Ġand Ġa Ġtest Ġset Ġof Ġ6 , 000 Ġtweets Ġ( 80 % / 20 % Ġtrain / test Ġsplit .) ĠEach Ġrecord Ġincludes Ġthe Ġraw Ġtweet Ġtext Ġ( including Ġaccents Ġand Ġemot icons ), Ġa Ġbinary Ġhumor Ġlabel , Ġthe Ġnumber Ġof Ġvotes Ġfor Ġeach Ġof Ġfive Ġstar Ġratings Ġand Ġa ĠâĢ ľ F unn iness ĠScore âĢ Ŀ Ġthat Ġis Ġthe Ġaverage Ġof Ġthe Ġ1 Ġto Ġ5 Ġstar Ġvotes Ġcast . ĠExamples Ġand Ġdata Ġcan Ġbe Ġfound Ġon Ġthe ĠC oda Lab Ġcompetition Ġwebpage . </s> System ĠDescription </s> We Ġmodify Ġthe Ġmethod Ġof ĠUniversal ĠLang age ĠModel ĠFine - tun ing Ġfor ĠText ĠClassification Ġ( UL M Fi T ) Ġpresented Ġin ĠHoward Ġand ĠRud er ĠB IB REF 3 Ġ. ĠThe Ġprimary Ġsteps Ġare : </s> Below Ġwe Ġwill Ġgive Ġmore Ġdetail Ġon Ġeach Ġstep Ġand Ġthe Ġparameters Ġused Ġto Ġgenerate Ġour Ġsystem . </s> Additional ĠData </s> We Ġcollected Ġa Ġcorpus Ġfor Ġour ĠLM Ġbased Ġon ĠSpanish ĠTwitter Ġusing Ġtwe ep y Ġrun Ġfor Ġthree Ġ4 - hour Ġsessions Ġand Ġcollecting Ġany Ġtweet Ġwith Ġany Ġof Ġthe Ġterms Ġ' el ',' su ',' lo ',' y ' Ġor Ġ' en '. ĠWe Ġexcluded Ġret we ets Ġto Ġminimize Ġrepeated Ġexamples Ġin Ġour Ġlanguage Ġmodel Ġtraining . ĠIn Ġtotal , Ġwe Ġcollected Ġ475 , 143 Ġtweets Ġ- Ġa Ġdata Ġset Ġis Ġnearly Ġ16 Ġtimes Ġlarger Ġthan Ġthe Ġtext Ġprovided Ġby Ġthe Ġcompetition Ġalone . ĠThe Ġfrequency Ġof Ġterms , Ġpunct uation Ġand Ġvocabulary Ġused Ġon ĠTwitter Ġcan Ġbe Ġquite Ġdifferent Ġfrom Ġthe Ġstandard ĠWikipedia Ġcorpus Ġthat Ġis Ġoften Ġused Ġto Ġtrain Ġan ĠLM Ġfrom Ġscratch . </s> In Ġthe Ġfine - tun ing Ġstep , Ġwe Ġcombined Ġthe Ġtrain Ġand Ġtest Ġtext Ġdata Ġwithout Ġlabels Ġfrom Ġthe Ġcontest Ġdata . </s> C leaning </s> We Ġapplied Ġa Ġlist Ġof Ġdefault Ġcleanup Ġfunctions Ġin Ġsequence Ġ( see Ġlist Ġbelow ). ĠThey Ġare Ġclose Ġto Ġthe Ġstandard Ġclean - up Ġincluded Ġin Ġthe ĠFast . ai Ġlibrary Ġwith Ġthe Ġaddition Ġof Ġone Ġfunction Ġfor Ġthe ĠTwitter Ġdataset . ĠClean up Ġof Ġdata Ġis Ġkey Ġto Ġexpressing Ġinformation Ġin Ġa Ġcompact Ġway Ġso Ġthat Ġthe ĠLM Ġcan Ġuse Ġthe Ġrelevant Ġdata Ġwhen Ġtrying Ġto Ġpredict Ġthe Ġnext Ġword Ġin Ġa Ġsequence . </s> Re place Ġmore Ġthan Ġ3 Ġrepet itions Ġof Ġthe Ġsame Ġcharacter Ġ( ie . Ġgr rr reat Ġbecomes Ġg Ġxx rep Ġr Ġ4 Ġeat ) </s> Re place Ġrepetition Ġat Ġthe Ġword Ġlevel Ġ( similar Ġto Ġabove ) </s> Deal Ġwith ĠALL ĠCAP S Ġwords Ġreplacing Ġwith Ġa Ġtoken Ġand Ġconverting Ġto Ġlower Ġcase . </s> Add Ġspaces Ġbetween Ġspecial Ġchars Ġ( ie . Ġ !!! Ġto Ġ! Ġ! Ġ! ) </s> Remove Ġuseless Ġspaces Ġ( remove Ġmore Ġthan Ġ2 Ġspaces Ġin Ġsequence ) </s> Add ition : ĠMove Ġall Ġtext Ġonto Ġa Ġsingle Ġline Ġby Ġreplacing Ġnew - lines Ġinside Ġa Ġtweet Ġwith Ġa Ġreserved Ġword Ġ( ie . Ġ\ n Ġto Ġxx nl ) </s> The Ġfollowing Ġexample Ġshows Ġthe Ġapplication Ġof Ġthis Ġdata Ġcleaning Ġto Ġa Ġsingle Ġtweet : </s> S aber , Ġent ender Ġy Ġest ar Ġconven c ides Ġque Ġla Ġfr ase Ġ\ </s> # La ES IL a Def end em os Ent re T odes Ġes Ġnu est ra Ġline a Ġes Ġnu estro Ġe je . \ </s> # Alert a ES I !!!! </s> V am os Ġpor Ġmas !!! Ġe Ġinv it am os Ġa Ġto d as Ġaqu ell as Ġperson as Ġque Ġqu ier an Ġ\ </s> se Ġpart e . </s> xx bos Ġsab er Ġ, Ġent ender Ġy Ġest ar Ġconven c ides Ġque Ġla Ġfr ase Ġ\ </s> # Ġla es il ade f end em os ent ret odes Ġes Ġnu est ra Ġline a Ġes Ġnu estro Ġe je . \ </s> xx nl Ġ# Ġalert a esi Ġxx rep Ġ4 Ġ! Ġxx nl Ġv am os Ġpor Ġmas Ġ! Ġ! Ġ! Ġe Ġinv it am os Ġa Ġ\ </s> t od as Ġaqu ell as Ġperson as Ġque Ġqu ier an Ġse Ġpart e . </s> Token ization </s> We Ġused Ġsentence piece ĠB IB REF 4 Ġto Ġparse Ġinto Ġsub - word Ġunits Ġand Ġreduce Ġthe Ġpossible Ġout - of - voc abulary Ġterms Ġin Ġthe Ġdata Ġset . ĠWe Ġselected Ġa Ġvoc ab Ġsize Ġof Ġ30 , 000 Ġand Ġused Ġthe Ġbyte - pair Ġencoding Ġ( B PE ) Ġmodel . ĠTo Ġour Ġknowledge Ġthis Ġis Ġthe Ġfirst Ġtime Ġthat Ġthe ĠB PE Ġto en ization Ġhas Ġbeen Ġused Ġwith ĠU LM Fi T Ġin Ġa Ġcompetition Ġmodel . </s> LM ĠTraining Ġand ĠFine - tun ing </s> We Ġtrain Ġthe ĠLM Ġusing Ġa Ġ90 / 10 Ġtraining / valid ation Ġsplit , Ġreporting Ġthe Ġvalidation Ġloss Ġand Ġaccuracy Ġof Ġnext - word Ġprediction Ġon Ġthe Ġvalidation Ġset . ĠFor Ġthe ĠLM , Ġwe Ġselected Ġan ĠAS GD ĠWeight - Dro pped ĠLong ĠShort ĠTerm ĠMemory Ġ( AW D _ L ST M , Ġdescribed Ġin ĠMer ity Ġet Ġal . ĠB IB REF 5 Ġ) Ġmodel Ġincluded Ġin ĠFast . ai . ĠWe Ġreplaced Ġthe Ġtypical ĠLong ĠShort ĠTerm ĠMemory Ġ( L ST M ) Ġunits Ġwith ĠQu asi ĠRec urrent ĠNeural ĠNetwork Ġ( Q R NN , Ġdescribed Ġin ĠBrad bury Ġet Ġal . ĠB IB REF 6 Ġ) Ġunits . ĠOur Ġnetwork Ġhas Ġ230 4 Ġhidden - states , Ġ3 Ġlayers Ġand Ġa Ġsoft max Ġlayer Ġto Ġpredict Ġthe Ġnext - word . ĠWe Ġtied Ġthe Ġembed ding Ġweights ĠB IB REF 7 Ġon Ġthe Ġenc oder Ġand Ġdec oder Ġfor Ġtraining . ĠWe Ġperformed Ġsome Ġsimple Ġtests Ġwith ĠL ST M Ġunits Ġand Ġa ĠTrans former ĠLanguage Ġmodel , Ġfinding Ġall Ġmodels Ġwere Ġsimilar Ġin Ġperformance Ġduring ĠLM Ġtraining . ĠWe Ġthus Ġchose Ġto Ġuse ĠQR NN Ġunits Ġdue Ġto Ġimproved Ġtraining Ġspeed Ġcompared Ġto Ġthe Ġalternatives . ĠThis Ġmodel Ġhas Ġabout Ġ60 Ġmillion Ġtrain able Ġparameters . </s> Parameters Ġused Ġfor Ġtraining Ġand Ġfine - tun ing Ġare Ġshown Ġin ĠTable ĠT AB REF 21 Ġ. ĠFor Ġall Ġnetworks Ġwe Ġapplied Ġa Ġdrop out Ġmultiplier Ġwhich Ġscales Ġthe Ġdrop out Ġused Ġthroughout Ġthe Ġnetwork . ĠWe Ġused Ġthe ĠAdam Ġoptim izer Ġwith Ġweight Ġdecay Ġas Ġindicated Ġin Ġthe Ġtable . </s> Following Ġthe Ġwork Ġof ĠSmith ĠB IB REF 8 Ġwe Ġfound Ġthe Ġlargest Ġlearning - rate Ġthat Ġwe Ġcould Ġapply Ġand Ġthen Ġran Ġa Ġone - cycle Ġpolicy Ġfor Ġa Ġsingle Ġepoch . ĠThis Ġlargest Ġweight Ġis Ġshown Ġin ĠTable ĠT AB REF 21 Ġunder Ġ" Learning ĠRate ." ĠSub sequent Ġtraining Ġepoch s Ġwere Ġrun Ġwith Ġone - cycle Ġand Ġlower Ġlearning Ġrates Ġindicated Ġin ĠTable ĠT AB REF 21 Ġunder Ġ" Contin ued ĠTraining ." </s> Class ification Ġand ĠReg ression ĠF itting </s> Again , Ġfollowing Ġthe Ġplay - book Ġfrom ĠHoward Ġand ĠRud er ĠB IB REF 3 Ġ, Ġwe Ġchange Ġthe Ġpre - trained Ġnetwork Ġhead Ġto Ġa Ġsoft max Ġor Ġlinear Ġoutput Ġlayer Ġ( as Ġappropriate Ġfor Ġthe Ġtransfer Ġtask ) Ġand Ġthen Ġload Ġthe ĠLM Ġweights Ġfor Ġthe Ġlayers Ġbelow . ĠWe Ġtrain Ġjust Ġthe Ġnew Ġhead Ġfrom Ġrandom Ġinitialization , Ġthen Ġunf ree ze Ġthe Ġentire Ġnetwork Ġand Ġtrain Ġwith Ġdifferential Ġlearning Ġrates . ĠWe Ġlayout Ġour Ġtraining Ġparameters Ġin ĠTable ĠT AB REF 25 Ġ. </s> With Ġthe Ġsame Ġlearning Ġrate Ġand Ġweight Ġdecay Ġwe Ġapply Ġa Ġ5 - fold Ġcross - valid ation Ġon Ġthe Ġoutputs Ġand Ġtake Ġthe Ġmean Ġacross Ġthe Ġfolds Ġas Ġour Ġensemble . ĠWe Ġsample Ġ20 Ġrandom Ġseeds Ġ( see Ġmore Ġin Ġsection ĠSEC REF 26 Ġ) Ġto Ġfind Ġthe Ġbest Ġinitialization Ġfor Ġour Ġgradient Ġdescent Ġsearch . ĠFrom Ġthese Ġsamples , Ġwe Ġselect Ġthe Ġbest Ġvalidation ĠF 1 Ġmetric Ġor ĠMean ĠSqu ared ĠError Ġ( M SE ) Ġfor Ġuse Ġin Ġour Ġtest Ġsubmission . </s> For Ġthe Ġclass ifier , Ġwe Ġhave Ġa Ġhidden Ġlayer Ġand Ġsoft max Ġhead . ĠWe Ġover - sample Ġthe Ġminority Ġclass Ġto Ġbalance Ġthe Ġoutcomes Ġfor Ġbetter Ġtraining Ġusing ĠSynt hetic ĠMinority ĠOvers am pling ĠTechnique Ġ( SM OTE , Ġdescribed Ġin ĠCh aw la Ġet Ġal . ĠB IB REF 9 Ġ). ĠOur Ġloss Ġis Ġlabel Ġsmoot hing Ġas Ġdescribed Ġin ĠPere y ra Ġet Ġal . ĠB IB REF 10 Ġof Ġthe Ġflattened Ġcross - ent ropy Ġloss . ĠIn ĠU LM Fi T , Ġgradual Ġunf ree zing Ġallows Ġus Ġto Ġavoid Ġcatast rop ic Ġforgetting , Ġfocus Ġeach Ġstage Ġof Ġtraining Ġand Ġpreventing Ġover - fitting Ġof Ġthe Ġparameters Ġto Ġthe Ġtraining Ġcases . ĠWe Ġtake Ġan Ġalternative Ġapproach Ġto Ġregular ization Ġand Ġin Ġour Ġexperiments Ġfound Ġthat Ġwe Ġgot Ġsimilar Ġresults Ġwith Ġlabel Ġsmoot hing Ġbut Ġwithout Ġthe Ġseparate Ġsteps Ġand Ġlearning Ġrate Ġrefinement Ġrequired Ġof Ġgradual Ġunf ree zing . </s> For Ġthe Ġregression Ġtask , Ġwe Ġfill Ġall Ġ# N / A Ġlabels Ġwith Ġscores Ġof Ġ0 . ĠWe Ġadd Ġa Ġhidden Ġlayer Ġand Ġlinear Ġoutput Ġhead Ġand ĠM SE Ġloss Ġfunction . </s> Random ĠSeed Ġas Ġa ĠHyper param ter </s> For Ġclassification Ġand Ġregression , Ġthe Ġrandom Ġseed Ġsets Ġthe Ġinitial Ġrandom Ġweights Ġof Ġthe Ġhead Ġlayer . ĠThis Ġinitialization Ġaffects Ġthe Ġfinal ĠF 1 Ġmetric Ġachievable . </s> Across Ġeach Ġof Ġthe Ġ20 Ġrandom Ġseeds , Ġwe Ġaverage Ġthe Ġ5 - f olds Ġand Ġobtain Ġa Ġsingle ĠF 1 Ġmetric Ġon Ġthe Ġvalidation Ġset . ĠThe Ġhist ogram Ġof Ġ20 - seed Ġoutcomes Ġis Ġshown Ġin ĠFigure ĠFIG REF 27 Ġand Ġcovers Ġa Ġrange Ġfrom Ġ0 . 820 Ġto Ġ0 . 825 Ġover Ġthe Ġvalidation Ġset . ĠWe Ġselected Ġour Ġsingle Ġbest Ġrandom Ġseed Ġfor Ġthe Ġtest Ġsubmission . ĠWith Ġmore Ġexploration , Ġa Ġbetter Ġseed Ġcould Ġlikely Ġbe Ġfound . ĠThough Ġwe Ġonly Ġuse Ġa Ġsingle Ġseed Ġfor Ġthe ĠLM Ġtraining , Ġone Ġcould Ġdo Ġa Ġsimilar Ġsearch Ġwith Ġrandom Ġseeds Ġfor ĠLM Ġpre - training , Ġand Ġfurther Ġselect Ġthe Ġbest Ġdown - stream Ġseed Ġsimilar Ġto ĠC za pl a Ġet Ġal ĠB IB REF 11 Ġ. </s> Results </s> Table ĠT AB REF 29 Ġgives Ġthree Ġresults Ġfrom Ġour Ġsubmissions Ġin Ġthe Ġcompetition . ĠThe Ġfirst Ġis Ġthe Ġbaseline ĠN BS VM Ġsolution , Ġwith Ġan ĠF 1 Ġof Ġ0 . 75 48 . ĠSecond Ġis Ġour Ġfirst Ġrandom Ġseed Ġselected Ġfor Ġthe Ġclass ifier Ġwhich Ġproduces Ġa Ġ0 . 80 83 Ġresult . ĠWhile Ġbetter Ġthan Ġthe ĠN BS VM Ġsolution , Ġwe Ġpick Ġthe Ġbest Ġvalidation ĠF 1 Ġfrom Ġthe Ġ20 Ġseeds Ġwe Ġtried . ĠThis Ġproduced Ġour Ġfinal Ġsubmission Ġof Ġ0 . 80 99 . ĠOur Ġbest Ġmodel Ġachieved Ġan Ġfive - fold Ġaverage ĠF 1 Ġof Ġ0 . 8 254 Ġon Ġthe Ġvalidation Ġset Ġshown Ġin ĠFigure ĠFIG REF 27 Ġbut Ġa Ġtest Ġset ĠF 1 Ġof Ġ0 . 80 99 Ġ- Ġa Ġdrop Ġof Ġ0 . 01 55 Ġin ĠF 1 Ġfor Ġthe Ġtrue Ġout - of - sample Ġdata . ĠAlso Ġnote Ġthat Ġour Ġthird Ġplace Ġentry Ġwas Ġ1 . 1 % Ġworse Ġin ĠF 1 Ġscore Ġthan Ġfirst Ġplace Ġbut Ġ1 . 2 % Ġbetter Ġin ĠF 1 Ġthan Ġthe Ġ4 th Ġplace Ġentry . </s> Conclusion </s> This Ġpaper Ġdescribes Ġour Ġimplementation Ġof Ġa Ġneural Ġnet Ġmodel Ġfor Ġclassification Ġand Ġregression Ġin Ġthe ĠHA HA Ġ2019 Ġchallenge . ĠOur Ġsolution Ġplaced Ġ3 rd Ġin ĠTask Ġ1 Ġand Ġ2 nd Ġin ĠTask Ġ2 Ġin Ġthe Ġfinal Ġcompetition Ġstandings . ĠWe Ġdescribe Ġthe Ġdata Ġcollection , Ġpre - training , Ġand Ġfinal Ġmodel Ġbuilding Ġsteps Ġfor Ġthis Ġcontest . ĠTwitter Ġhas Ġslang Ġand Ġabbrevi ations Ġthat Ġare Ġunique Ġto Ġthe Ġshort - format Ġas Ġwell Ġas Ġgenerous Ġuse Ġof Ġemot icons . ĠTo Ġcapture Ġthese Ġfeatures , Ġwe Ġcollected Ġour Ġown Ġdataset Ġbased Ġon ĠSpanish ĠTwe ets Ġthat Ġis Ġ16 Ġtimes Ġlarger Ġthan Ġthe Ġcompetition Ġdata Ġset Ġand Ġallowed Ġus Ġto Ġpre - train Ġa Ġlanguage Ġmodel . ĠHum or Ġis Ġsubtle Ġand Ġusing Ġa Ġlabel Ġsmoot hed Ġloss Ġprevented Ġus Ġfrom Ġbecoming Ġover conf ident Ġin Ġour Ġpredictions Ġand Ġtrain Ġmore Ġquickly Ġwithout Ġthe Ġgradual Ġunf ree zing Ġrequired Ġby ĠU LM Fi T . ĠWe Ġhave Ġopen - s ourced Ġall Ġcode Ġused Ġin Ġthis Ġcontest Ġto Ġfurther Ġenable Ġresearch Ġon Ġthis Ġtask Ġin Ġthe Ġfuture . </s> Author ĠContributions </s> BF Ġwas Ġthe Ġprimary Ġresearcher . ĠPC Ġcontributed Ġwith Ġsuggestions Ġfor Ġthe Ġrandom Ġseeds Ġas Ġa Ġhyper - param eters Ġand Ġlabel Ġsmoot hing Ġto Ġspeed Ġup Ġtraining . ĠJ H Ġcontributed Ġwith Ġsuggestion Ġfor Ġhigher Ġdrop out Ġthroughout Ġthe Ġnetwork Ġfor Ġmore Ġgeneral ization . </s> Acknowled gements </s> The Ġauthor Ġwould Ġlike Ġto Ġthank Ġall Ġthe Ġparticipants Ġon Ġthe Ġfast . ai Ġforums Ġfor Ġtheir Ġideas Ġand Ġsuggestions . ĠAlso , ĠKyle ĠK ast ner Ġfor Ġhis Ġedits , Ġsuggestions Ġand Ġrecommendations Ġin Ġwriting Ġup Ġthese Ġresults .
Introduction - !`Socorro, me ha picado una víbora! - ?`Cobra? - No, gratis.[5] Google Translation: - Help, I was bitten by a snake! - Does it charge? - Not free. [4]https://github.com/bfarzin/haha_2019_final, Accessed on 19 June 2019 [5]https://www.fluentin3months.com/spanish-jokes/, Accessed on 19 June 2019 Humor does not translate well because it often relies on double-meaning or a subtle play on word choice, pronunciation, or context. These issues are further exacerbated in areas where space is a premium (as frequent on social media platforms), often leading to usage and development of shorthand, in-jokes, and self-reference. Thus, building a system to classify the humor of tweets is a difficult task. However, with transfer-learning and the Fast.ai library, we can build a high quality classifier in a foreign language. Our system outperforms a Naive Bayes Support Vector Machine (NBSVM) baseline, which is frequently considered a "strong baseline" for many Natural Language Processing (NLP) related tasks (see Wang et al BIBREF0 ). Rather than hand-crafted language features, we have taken an "end to end" approach building from the raw text to a final model that achieves the tasks as presented. Our paper lays out the details of the system and our code can be found in a GitHub repository for use by other researchers to extend the state of the art in sentiment analysis. Task and Dataset Description The Humor Analysis based on Human Annotation (HAHA) 2019 BIBREF1 competition asked for analysis of two tasks in the Spanish language based on a corpus of publicly collected data described in Castro et al. BIBREF2 : The HAHA dataset includes labeled data for 24,000 tweets and a test set of 6,000 tweets (80%/20% train/test split.) Each record includes the raw tweet text (including accents and emoticons), a binary humor label, the number of votes for each of five star ratings and a “Funniness Score” that is the average of the 1 to 5 star votes cast. Examples and data can be found on the CodaLab competition webpage. System Description We modify the method of Universal Langage Model Fine-tuning for Text Classification (ULMFiT) presented in Howard and Ruder BIBREF3 . The primary steps are: Below we will give more detail on each step and the parameters used to generate our system. Additional Data We collected a corpus for our LM based on Spanish Twitter using tweepy run for three 4-hour sessions and collecting any tweet with any of the terms 'el','su','lo','y' or 'en'. We excluded retweets to minimize repeated examples in our language model training. In total, we collected 475,143 tweets - a data set is nearly 16 times larger than the text provided by the competition alone. The frequency of terms, punctuation and vocabulary used on Twitter can be quite different from the standard Wikipedia corpus that is often used to train an LM from scratch. In the fine-tuning step, we combined the train and test text data without labels from the contest data. Cleaning We applied a list of default cleanup functions in sequence (see list below). They are close to the standard clean-up included in the Fast.ai library with the addition of one function for the Twitter dataset. Cleanup of data is key to expressing information in a compact way so that the LM can use the relevant data when trying to predict the next word in a sequence. Replace more than 3 repetitions of the same character (ie. grrrreat becomes g xxrep r 4 eat) Replace repetition at the word level (similar to above) Deal with ALL CAPS words replacing with a token and converting to lower case. Add spaces between special chars (ie. !!! to ! ! !) Remove useless spaces (remove more than 2 spaces in sequence) Addition: Move all text onto a single line by replacing new-lines inside a tweet with a reserved word (ie. \n to xxnl) The following example shows the application of this data cleaning to a single tweet: Saber, entender y estar convencides que la frase \ #LaESILaDefendemosEntreTodes es nuestra linea es nuestro eje.\ #AlertaESI!!!! Vamos por mas!!! e invitamos a todas aquellas personas que quieran \ se parte. xxbos saber , entender y estar convencides que la frase \ # laesiladefendemosentretodes es nuestra linea es nuestro eje.\ xxnl # alertaesi xxrep 4 ! xxnl vamos por mas ! ! ! e invitamos a \ todas aquellas personas que quieran se parte. Tokenization We used sentencepiece BIBREF4 to parse into sub-word units and reduce the possible out-of-vocabulary terms in the data set. We selected a vocab size of 30,000 and used the byte-pair encoding (BPE) model. To our knowledge this is the first time that the BPE toenization has been used with ULMFiT in a competition model. LM Training and Fine-tuning We train the LM using a 90/10 training/validation split, reporting the validation loss and accuracy of next-word prediction on the validation set. For the LM, we selected an ASGD Weight-Dropped Long Short Term Memory (AWD_LSTM, described in Merity et al. BIBREF5 ) model included in Fast.ai. We replaced the typical Long Short Term Memory (LSTM) units with Quasi Recurrent Neural Network (QRNN, described in Bradbury et al. BIBREF6 ) units. Our network has 2304 hidden-states, 3 layers and a softmax layer to predict the next-word. We tied the embedding weights BIBREF7 on the encoder and decoder for training. We performed some simple tests with LSTM units and a Transformer Language model, finding all models were similar in performance during LM training. We thus chose to use QRNN units due to improved training speed compared to the alternatives. This model has about 60 million trainable parameters. Parameters used for training and fine-tuning are shown in Table TABREF21 . For all networks we applied a dropout multiplier which scales the dropout used throughout the network. We used the Adam optimizer with weight decay as indicated in the table. Following the work of Smith BIBREF8 we found the largest learning-rate that we could apply and then ran a one-cycle policy for a single epoch. This largest weight is shown in Table TABREF21 under "Learning Rate." Subsequent training epochs were run with one-cycle and lower learning rates indicated in Table TABREF21 under "Continued Training." Classification and Regression Fitting Again, following the play-book from Howard and Ruder BIBREF3 , we change the pre-trained network head to a softmax or linear output layer (as appropriate for the transfer task) and then load the LM weights for the layers below. We train just the new head from random initialization, then unfreeze the entire network and train with differential learning rates. We layout our training parameters in Table TABREF25 . With the same learning rate and weight decay we apply a 5-fold cross-validation on the outputs and take the mean across the folds as our ensemble. We sample 20 random seeds (see more in section SECREF26 ) to find the best initialization for our gradient descent search. From these samples, we select the best validation F1 metric or Mean Squared Error (MSE) for use in our test submission. For the classifier, we have a hidden layer and softmax head. We over-sample the minority class to balance the outcomes for better training using Synthetic Minority Oversampling Technique (SMOTE, described in Chawla et al. BIBREF9 ). Our loss is label smoothing as described in Pereyra et al. BIBREF10 of the flattened cross-entropy loss. In ULMFiT, gradual unfreezing allows us to avoid catastropic forgetting, focus each stage of training and preventing over-fitting of the parameters to the training cases. We take an alternative approach to regularization and in our experiments found that we got similar results with label smoothing but without the separate steps and learning rate refinement required of gradual unfreezing. For the regression task, we fill all #N/A labels with scores of 0. We add a hidden layer and linear output head and MSE loss function. Random Seed as a Hyperparamter For classification and regression, the random seed sets the initial random weights of the head layer. This initialization affects the final F1 metric achievable. Across each of the 20 random seeds, we average the 5-folds and obtain a single F1 metric on the validation set. The histogram of 20-seed outcomes is shown in Figure FIGREF27 and covers a range from 0.820 to 0.825 over the validation set. We selected our single best random seed for the test submission. With more exploration, a better seed could likely be found. Though we only use a single seed for the LM training, one could do a similar search with random seeds for LM pre-training, and further select the best down-stream seed similar to Czapla et al BIBREF11 . Results Table TABREF29 gives three results from our submissions in the competition. The first is the baseline NBSVM solution, with an F1 of 0.7548. Second is our first random seed selected for the classifier which produces a 0.8083 result. While better than the NBSVM solution, we pick the best validation F1 from the 20 seeds we tried. This produced our final submission of 0.8099. Our best model achieved an five-fold average F1 of 0.8254 on the validation set shown in Figure FIGREF27 but a test set F1 of 0.8099 - a drop of 0.0155 in F1 for the true out-of-sample data. Also note that our third place entry was 1.1% worse in F1 score than first place but 1.2% better in F1 than the 4th place entry. Conclusion This paper describes our implementation of a neural net model for classification and regression in the HAHA 2019 challenge. Our solution placed 3rd in Task 1 and 2nd in Task 2 in the final competition standings. We describe the data collection, pre-training, and final model building steps for this contest. Twitter has slang and abbreviations that are unique to the short-format as well as generous use of emoticons. To capture these features, we collected our own dataset based on Spanish Tweets that is 16 times larger than the competition data set and allowed us to pre-train a language model. Humor is subtle and using a label smoothed loss prevented us from becoming overconfident in our predictions and train more quickly without the gradual unfreezing required by ULMFiT. We have open-sourced all code used in this contest to further enable research on this task in the future. Author Contributions BF was the primary researcher. PC contributed with suggestions for the random seeds as a hyper-parameters and label smoothing to speed up training. JH contributed with suggestion for higher dropout throughout the network for more generalization. Acknowledgements The author would like to thank all the participants on the fast.ai forums for their ideas and suggestions. Also, Kyle Kastner for his edits, suggestions and recommendations in writing up these results.
What did the best systems use for their model?
b7c3f3942a07c118e57130bc4c3ec4adc431d725
What Ġdid Ġthe Ġbest Ġsystems Ġuse Ġfor Ġtheir Ġmodel ?
Un answer able
<s> What Ġdid Ġthe Ġbest Ġsystems Ġuse Ġfor Ġtheir Ġmodel ? </s> Introduction </s> - Ġ! ` Soc or ro , Ġme Ġha Ġpic ado Ġun a Ġv ÃŃ bor a ! </s> - Ġ? ` C ob ra ? </s> - ĠNo , Ġgrat is .[ 5 ] </s> Google ĠTranslation : </s> - ĠHelp , ĠI Ġwas Ġbitten Ġby Ġa Ġsnake ! </s> - ĠDoes Ġit Ġcharge ? </s> - ĠNot Ġfree . </s> [ 4 ] https :// github . com / b far zin / h aha _ 2019 _ final , ĠAccessed Ġon Ġ19 ĠJune Ġ2019 Ġ[ 5 ] https :// www . f luent in 3 months . com / sp anish - j okes /, ĠAccessed Ġon Ġ19 ĠJune Ġ2019 </s> Hum or Ġdoes Ġnot Ġtranslate Ġwell Ġbecause Ġit Ġoften Ġrelies Ġon Ġdouble - meaning Ġor Ġa Ġsubtle Ġplay Ġon Ġword Ġchoice , Ġpronunciation , Ġor Ġcontext . ĠThese Ġissues Ġare Ġfurther Ġexacerbated Ġin Ġareas Ġwhere Ġspace Ġis Ġa Ġpremium Ġ( as Ġfrequent Ġon Ġsocial Ġmedia Ġplatforms ), Ġoften Ġleading Ġto Ġusage Ġand Ġdevelopment Ġof Ġshorthand , Ġin - j okes , Ġand Ġself - reference . ĠThus , Ġbuilding Ġa Ġsystem Ġto Ġclassify Ġthe Ġhumor Ġof Ġtweets Ġis Ġa Ġdifficult Ġtask . ĠHowever , Ġwith Ġtransfer - learning Ġand Ġthe ĠFast . ai Ġlibrary , Ġwe Ġcan Ġbuild Ġa Ġhigh Ġquality Ġclass ifier Ġin Ġa Ġforeign Ġlanguage . ĠOur Ġsystem Ġoutper forms Ġa ĠNa ive ĠBay es ĠSupport ĠVector ĠMachine Ġ( N BS VM ) Ġbaseline , Ġwhich Ġis Ġfrequently Ġconsidered Ġa Ġ" strong Ġbaseline " Ġfor Ġmany ĠNatural ĠLanguage ĠProcessing Ġ( N LP ) Ġrelated Ġtasks Ġ( see ĠWang Ġet Ġal ĠB IB REF 0 Ġ). </s> Rather Ġthan Ġhand - crafted Ġlanguage Ġfeatures , Ġwe Ġhave Ġtaken Ġan Ġ" end Ġto Ġend " Ġapproach Ġbuilding Ġfrom Ġthe Ġraw Ġtext Ġto Ġa Ġfinal Ġmodel Ġthat Ġachieves Ġthe Ġtasks Ġas Ġpresented . ĠOur Ġpaper Ġlays Ġout Ġthe Ġdetails Ġof Ġthe Ġsystem Ġand Ġour Ġcode Ġcan Ġbe Ġfound Ġin Ġa ĠGitHub Ġrepository Ġfor Ġuse Ġby Ġother Ġresearchers Ġto Ġextend Ġthe Ġstate Ġof Ġthe Ġart Ġin Ġsentiment Ġanalysis . </s> Task Ġand ĠDat as et ĠDescription </s> The ĠHum or ĠAnalysis Ġbased Ġon ĠHuman ĠAn notation Ġ( HAHA ) Ġ2019 ĠB IB REF 1 Ġcompetition Ġasked Ġfor Ġanalysis Ġof Ġtwo Ġtasks Ġin Ġthe ĠSpanish Ġlanguage Ġbased Ġon Ġa Ġcorpus Ġof Ġpublicly Ġcollected Ġdata Ġdescribed Ġin ĠCastro Ġet Ġal . ĠB IB REF 2 Ġ: </s> The ĠHA HA Ġdataset Ġincludes Ġlabeled Ġdata Ġfor Ġ24 , 000 Ġtweets Ġand Ġa Ġtest Ġset Ġof Ġ6 , 000 Ġtweets Ġ( 80 % / 20 % Ġtrain / test Ġsplit .) ĠEach Ġrecord Ġincludes Ġthe Ġraw Ġtweet Ġtext Ġ( including Ġaccents Ġand Ġemot icons ), Ġa Ġbinary Ġhumor Ġlabel , Ġthe Ġnumber Ġof Ġvotes Ġfor Ġeach Ġof Ġfive Ġstar Ġratings Ġand Ġa ĠâĢ ľ F unn iness ĠScore âĢ Ŀ Ġthat Ġis Ġthe Ġaverage Ġof Ġthe Ġ1 Ġto Ġ5 Ġstar Ġvotes Ġcast . ĠExamples Ġand Ġdata Ġcan Ġbe Ġfound Ġon Ġthe ĠC oda Lab Ġcompetition Ġwebpage . </s> System ĠDescription </s> We Ġmodify Ġthe Ġmethod Ġof ĠUniversal ĠLang age ĠModel ĠFine - tun ing Ġfor ĠText ĠClassification Ġ( UL M Fi T ) Ġpresented Ġin ĠHoward Ġand ĠRud er ĠB IB REF 3 Ġ. ĠThe Ġprimary Ġsteps Ġare : </s> Below Ġwe Ġwill Ġgive Ġmore Ġdetail Ġon Ġeach Ġstep Ġand Ġthe Ġparameters Ġused Ġto Ġgenerate Ġour Ġsystem . </s> Additional ĠData </s> We Ġcollected Ġa Ġcorpus Ġfor Ġour ĠLM Ġbased Ġon ĠSpanish ĠTwitter Ġusing Ġtwe ep y Ġrun Ġfor Ġthree Ġ4 - hour Ġsessions Ġand Ġcollecting Ġany Ġtweet Ġwith Ġany Ġof Ġthe Ġterms Ġ' el ',' su ',' lo ',' y ' Ġor Ġ' en '. ĠWe Ġexcluded Ġret we ets Ġto Ġminimize Ġrepeated Ġexamples Ġin Ġour Ġlanguage Ġmodel Ġtraining . ĠIn Ġtotal , Ġwe Ġcollected Ġ475 , 143 Ġtweets Ġ- Ġa Ġdata Ġset Ġis Ġnearly Ġ16 Ġtimes Ġlarger Ġthan Ġthe Ġtext Ġprovided Ġby Ġthe Ġcompetition Ġalone . ĠThe Ġfrequency Ġof Ġterms , Ġpunct uation Ġand Ġvocabulary Ġused Ġon ĠTwitter Ġcan Ġbe Ġquite Ġdifferent Ġfrom Ġthe Ġstandard ĠWikipedia Ġcorpus Ġthat Ġis Ġoften Ġused Ġto Ġtrain Ġan ĠLM Ġfrom Ġscratch . </s> In Ġthe Ġfine - tun ing Ġstep , Ġwe Ġcombined Ġthe Ġtrain Ġand Ġtest Ġtext Ġdata Ġwithout Ġlabels Ġfrom Ġthe Ġcontest Ġdata . </s> C leaning </s> We Ġapplied Ġa Ġlist Ġof Ġdefault Ġcleanup Ġfunctions Ġin Ġsequence Ġ( see Ġlist Ġbelow ). ĠThey Ġare Ġclose Ġto Ġthe Ġstandard Ġclean - up Ġincluded Ġin Ġthe ĠFast . ai Ġlibrary Ġwith Ġthe Ġaddition Ġof Ġone Ġfunction Ġfor Ġthe ĠTwitter Ġdataset . ĠClean up Ġof Ġdata Ġis Ġkey Ġto Ġexpressing Ġinformation Ġin Ġa Ġcompact Ġway Ġso Ġthat Ġthe ĠLM Ġcan Ġuse Ġthe Ġrelevant Ġdata Ġwhen Ġtrying Ġto Ġpredict Ġthe Ġnext Ġword Ġin Ġa Ġsequence . </s> Re place Ġmore Ġthan Ġ3 Ġrepet itions Ġof Ġthe Ġsame Ġcharacter Ġ( ie . Ġgr rr reat Ġbecomes Ġg Ġxx rep Ġr Ġ4 Ġeat ) </s> Re place Ġrepetition Ġat Ġthe Ġword Ġlevel Ġ( similar Ġto Ġabove ) </s> Deal Ġwith ĠALL ĠCAP S Ġwords Ġreplacing Ġwith Ġa Ġtoken Ġand Ġconverting Ġto Ġlower Ġcase . </s> Add Ġspaces Ġbetween Ġspecial Ġchars Ġ( ie . Ġ !!! Ġto Ġ! Ġ! Ġ! ) </s> Remove Ġuseless Ġspaces Ġ( remove Ġmore Ġthan Ġ2 Ġspaces Ġin Ġsequence ) </s> Add ition : ĠMove Ġall Ġtext Ġonto Ġa Ġsingle Ġline Ġby Ġreplacing Ġnew - lines Ġinside Ġa Ġtweet Ġwith Ġa Ġreserved Ġword Ġ( ie . Ġ\ n Ġto Ġxx nl ) </s> The Ġfollowing Ġexample Ġshows Ġthe Ġapplication Ġof Ġthis Ġdata Ġcleaning Ġto Ġa Ġsingle Ġtweet : </s> S aber , Ġent ender Ġy Ġest ar Ġconven c ides Ġque Ġla Ġfr ase Ġ\ </s> # La ES IL a Def end em os Ent re T odes Ġes Ġnu est ra Ġline a Ġes Ġnu estro Ġe je . \ </s> # Alert a ES I !!!! </s> V am os Ġpor Ġmas !!! Ġe Ġinv it am os Ġa Ġto d as Ġaqu ell as Ġperson as Ġque Ġqu ier an Ġ\ </s> se Ġpart e . </s> xx bos Ġsab er Ġ, Ġent ender Ġy Ġest ar Ġconven c ides Ġque Ġla Ġfr ase Ġ\ </s> # Ġla es il ade f end em os ent ret odes Ġes Ġnu est ra Ġline a Ġes Ġnu estro Ġe je . \ </s> xx nl Ġ# Ġalert a esi Ġxx rep Ġ4 Ġ! Ġxx nl Ġv am os Ġpor Ġmas Ġ! Ġ! Ġ! Ġe Ġinv it am os Ġa Ġ\ </s> t od as Ġaqu ell as Ġperson as Ġque Ġqu ier an Ġse Ġpart e . </s> Token ization </s> We Ġused Ġsentence piece ĠB IB REF 4 Ġto Ġparse Ġinto Ġsub - word Ġunits Ġand Ġreduce Ġthe Ġpossible Ġout - of - voc abulary Ġterms Ġin Ġthe Ġdata Ġset . ĠWe Ġselected Ġa Ġvoc ab Ġsize Ġof Ġ30 , 000 Ġand Ġused Ġthe Ġbyte - pair Ġencoding Ġ( B PE ) Ġmodel . ĠTo Ġour Ġknowledge Ġthis Ġis Ġthe Ġfirst Ġtime Ġthat Ġthe ĠB PE Ġto en ization Ġhas Ġbeen Ġused Ġwith ĠU LM Fi T Ġin Ġa Ġcompetition Ġmodel . </s> LM ĠTraining Ġand ĠFine - tun ing </s> We Ġtrain Ġthe ĠLM Ġusing Ġa Ġ90 / 10 Ġtraining / valid ation Ġsplit , Ġreporting Ġthe Ġvalidation Ġloss Ġand Ġaccuracy Ġof Ġnext - word Ġprediction Ġon Ġthe Ġvalidation Ġset . ĠFor Ġthe ĠLM , Ġwe Ġselected Ġan ĠAS GD ĠWeight - Dro pped ĠLong ĠShort ĠTerm ĠMemory Ġ( AW D _ L ST M , Ġdescribed Ġin ĠMer ity Ġet Ġal . ĠB IB REF 5 Ġ) Ġmodel Ġincluded Ġin ĠFast . ai . ĠWe Ġreplaced Ġthe Ġtypical ĠLong ĠShort ĠTerm ĠMemory Ġ( L ST M ) Ġunits Ġwith ĠQu asi ĠRec urrent ĠNeural ĠNetwork Ġ( Q R NN , Ġdescribed Ġin ĠBrad bury Ġet Ġal . ĠB IB REF 6 Ġ) Ġunits . ĠOur Ġnetwork Ġhas Ġ230 4 Ġhidden - states , Ġ3 Ġlayers Ġand Ġa Ġsoft max Ġlayer Ġto Ġpredict Ġthe Ġnext - word . ĠWe Ġtied Ġthe Ġembed ding Ġweights ĠB IB REF 7 Ġon Ġthe Ġenc oder Ġand Ġdec oder Ġfor Ġtraining . ĠWe Ġperformed Ġsome Ġsimple Ġtests Ġwith ĠL ST M Ġunits Ġand Ġa ĠTrans former ĠLanguage Ġmodel , Ġfinding Ġall Ġmodels Ġwere Ġsimilar Ġin Ġperformance Ġduring ĠLM Ġtraining . ĠWe Ġthus Ġchose Ġto Ġuse ĠQR NN Ġunits Ġdue Ġto Ġimproved Ġtraining Ġspeed Ġcompared Ġto Ġthe Ġalternatives . ĠThis Ġmodel Ġhas Ġabout Ġ60 Ġmillion Ġtrain able Ġparameters . </s> Parameters Ġused Ġfor Ġtraining Ġand Ġfine - tun ing Ġare Ġshown Ġin ĠTable ĠT AB REF 21 Ġ. ĠFor Ġall Ġnetworks Ġwe Ġapplied Ġa Ġdrop out Ġmultiplier Ġwhich Ġscales Ġthe Ġdrop out Ġused Ġthroughout Ġthe Ġnetwork . ĠWe Ġused Ġthe ĠAdam Ġoptim izer Ġwith Ġweight Ġdecay Ġas Ġindicated Ġin Ġthe Ġtable . </s> Following Ġthe Ġwork Ġof ĠSmith ĠB IB REF 8 Ġwe Ġfound Ġthe Ġlargest Ġlearning - rate Ġthat Ġwe Ġcould Ġapply Ġand Ġthen Ġran Ġa Ġone - cycle Ġpolicy Ġfor Ġa Ġsingle Ġepoch . ĠThis Ġlargest Ġweight Ġis Ġshown Ġin ĠTable ĠT AB REF 21 Ġunder Ġ" Learning ĠRate ." ĠSub sequent Ġtraining Ġepoch s Ġwere Ġrun Ġwith Ġone - cycle Ġand Ġlower Ġlearning Ġrates Ġindicated Ġin ĠTable ĠT AB REF 21 Ġunder Ġ" Contin ued ĠTraining ." </s> Class ification Ġand ĠReg ression ĠF itting </s> Again , Ġfollowing Ġthe Ġplay - book Ġfrom ĠHoward Ġand ĠRud er ĠB IB REF 3 Ġ, Ġwe Ġchange Ġthe Ġpre - trained Ġnetwork Ġhead Ġto Ġa Ġsoft max Ġor Ġlinear Ġoutput Ġlayer Ġ( as Ġappropriate Ġfor Ġthe Ġtransfer Ġtask ) Ġand Ġthen Ġload Ġthe ĠLM Ġweights Ġfor Ġthe Ġlayers Ġbelow . ĠWe Ġtrain Ġjust Ġthe Ġnew Ġhead Ġfrom Ġrandom Ġinitialization , Ġthen Ġunf ree ze Ġthe Ġentire Ġnetwork Ġand Ġtrain Ġwith Ġdifferential Ġlearning Ġrates . ĠWe Ġlayout Ġour Ġtraining Ġparameters Ġin ĠTable ĠT AB REF 25 Ġ. </s> With Ġthe Ġsame Ġlearning Ġrate Ġand Ġweight Ġdecay Ġwe Ġapply Ġa Ġ5 - fold Ġcross - valid ation Ġon Ġthe Ġoutputs Ġand Ġtake Ġthe Ġmean Ġacross Ġthe Ġfolds Ġas Ġour Ġensemble . ĠWe Ġsample Ġ20 Ġrandom Ġseeds Ġ( see Ġmore Ġin Ġsection ĠSEC REF 26 Ġ) Ġto Ġfind Ġthe Ġbest Ġinitialization Ġfor Ġour Ġgradient Ġdescent Ġsearch . ĠFrom Ġthese Ġsamples , Ġwe Ġselect Ġthe Ġbest Ġvalidation ĠF 1 Ġmetric Ġor ĠMean ĠSqu ared ĠError Ġ( M SE ) Ġfor Ġuse Ġin Ġour Ġtest Ġsubmission . </s> For Ġthe Ġclass ifier , Ġwe Ġhave Ġa Ġhidden Ġlayer Ġand Ġsoft max Ġhead . ĠWe Ġover - sample Ġthe Ġminority Ġclass Ġto Ġbalance Ġthe Ġoutcomes Ġfor Ġbetter Ġtraining Ġusing ĠSynt hetic ĠMinority ĠOvers am pling ĠTechnique Ġ( SM OTE , Ġdescribed Ġin ĠCh aw la Ġet Ġal . ĠB IB REF 9 Ġ). ĠOur Ġloss Ġis Ġlabel Ġsmoot hing Ġas Ġdescribed Ġin ĠPere y ra Ġet Ġal . ĠB IB REF 10 Ġof Ġthe Ġflattened Ġcross - ent ropy Ġloss . ĠIn ĠU LM Fi T , Ġgradual Ġunf ree zing Ġallows Ġus Ġto Ġavoid Ġcatast rop ic Ġforgetting , Ġfocus Ġeach Ġstage Ġof Ġtraining Ġand Ġpreventing Ġover - fitting Ġof Ġthe Ġparameters Ġto Ġthe Ġtraining Ġcases . ĠWe Ġtake Ġan Ġalternative Ġapproach Ġto Ġregular ization Ġand Ġin Ġour Ġexperiments Ġfound Ġthat Ġwe Ġgot Ġsimilar Ġresults Ġwith Ġlabel Ġsmoot hing Ġbut Ġwithout Ġthe Ġseparate Ġsteps Ġand Ġlearning Ġrate Ġrefinement Ġrequired Ġof Ġgradual Ġunf ree zing . </s> For Ġthe Ġregression Ġtask , Ġwe Ġfill Ġall Ġ# N / A Ġlabels Ġwith Ġscores Ġof Ġ0 . ĠWe Ġadd Ġa Ġhidden Ġlayer Ġand Ġlinear Ġoutput Ġhead Ġand ĠM SE Ġloss Ġfunction . </s> Random ĠSeed Ġas Ġa ĠHyper param ter </s> For Ġclassification Ġand Ġregression , Ġthe Ġrandom Ġseed Ġsets Ġthe Ġinitial Ġrandom Ġweights Ġof Ġthe Ġhead Ġlayer . ĠThis Ġinitialization Ġaffects Ġthe Ġfinal ĠF 1 Ġmetric Ġachievable . </s> Across Ġeach Ġof Ġthe Ġ20 Ġrandom Ġseeds , Ġwe Ġaverage Ġthe Ġ5 - f olds Ġand Ġobtain Ġa Ġsingle ĠF 1 Ġmetric Ġon Ġthe Ġvalidation Ġset . ĠThe Ġhist ogram Ġof Ġ20 - seed Ġoutcomes Ġis Ġshown Ġin ĠFigure ĠFIG REF 27 Ġand Ġcovers Ġa Ġrange Ġfrom Ġ0 . 820 Ġto Ġ0 . 825 Ġover Ġthe Ġvalidation Ġset . ĠWe Ġselected Ġour Ġsingle Ġbest Ġrandom Ġseed Ġfor Ġthe Ġtest Ġsubmission . ĠWith Ġmore Ġexploration , Ġa Ġbetter Ġseed Ġcould Ġlikely Ġbe Ġfound . ĠThough Ġwe Ġonly Ġuse Ġa Ġsingle Ġseed Ġfor Ġthe ĠLM Ġtraining , Ġone Ġcould Ġdo Ġa Ġsimilar Ġsearch Ġwith Ġrandom Ġseeds Ġfor ĠLM Ġpre - training , Ġand Ġfurther Ġselect Ġthe Ġbest Ġdown - stream Ġseed Ġsimilar Ġto ĠC za pl a Ġet Ġal ĠB IB REF 11 Ġ. </s> Results </s> Table ĠT AB REF 29 Ġgives Ġthree Ġresults Ġfrom Ġour Ġsubmissions Ġin Ġthe Ġcompetition . ĠThe Ġfirst Ġis Ġthe Ġbaseline ĠN BS VM Ġsolution , Ġwith Ġan ĠF 1 Ġof Ġ0 . 75 48 . ĠSecond Ġis Ġour Ġfirst Ġrandom Ġseed Ġselected Ġfor Ġthe Ġclass ifier Ġwhich Ġproduces Ġa Ġ0 . 80 83 Ġresult . ĠWhile Ġbetter Ġthan Ġthe ĠN BS VM Ġsolution , Ġwe Ġpick Ġthe Ġbest Ġvalidation ĠF 1 Ġfrom Ġthe Ġ20 Ġseeds Ġwe Ġtried . ĠThis Ġproduced Ġour Ġfinal Ġsubmission Ġof Ġ0 . 80 99 . ĠOur Ġbest Ġmodel Ġachieved Ġan Ġfive - fold Ġaverage ĠF 1 Ġof Ġ0 . 8 254 Ġon Ġthe Ġvalidation Ġset Ġshown Ġin ĠFigure ĠFIG REF 27 Ġbut Ġa Ġtest Ġset ĠF 1 Ġof Ġ0 . 80 99 Ġ- Ġa Ġdrop Ġof Ġ0 . 01 55 Ġin ĠF 1 Ġfor Ġthe Ġtrue Ġout - of - sample Ġdata . ĠAlso Ġnote Ġthat Ġour Ġthird Ġplace Ġentry Ġwas Ġ1 . 1 % Ġworse Ġin ĠF 1 Ġscore Ġthan Ġfirst Ġplace Ġbut Ġ1 . 2 % Ġbetter Ġin ĠF 1 Ġthan Ġthe Ġ4 th Ġplace Ġentry . </s> Conclusion </s> This Ġpaper Ġdescribes Ġour Ġimplementation Ġof Ġa Ġneural Ġnet Ġmodel Ġfor Ġclassification Ġand Ġregression Ġin Ġthe ĠHA HA Ġ2019 Ġchallenge . ĠOur Ġsolution Ġplaced Ġ3 rd Ġin ĠTask Ġ1 Ġand Ġ2 nd Ġin ĠTask Ġ2 Ġin Ġthe Ġfinal Ġcompetition Ġstandings . ĠWe Ġdescribe Ġthe Ġdata Ġcollection , Ġpre - training , Ġand Ġfinal Ġmodel Ġbuilding Ġsteps Ġfor Ġthis Ġcontest . ĠTwitter Ġhas Ġslang Ġand Ġabbrevi ations Ġthat Ġare Ġunique Ġto Ġthe Ġshort - format Ġas Ġwell Ġas Ġgenerous Ġuse Ġof Ġemot icons . ĠTo Ġcapture Ġthese Ġfeatures , Ġwe Ġcollected Ġour Ġown Ġdataset Ġbased Ġon ĠSpanish ĠTwe ets Ġthat Ġis Ġ16 Ġtimes Ġlarger Ġthan Ġthe Ġcompetition Ġdata Ġset Ġand Ġallowed Ġus Ġto Ġpre - train Ġa Ġlanguage Ġmodel . ĠHum or Ġis Ġsubtle Ġand Ġusing Ġa Ġlabel Ġsmoot hed Ġloss Ġprevented Ġus Ġfrom Ġbecoming Ġover conf ident Ġin Ġour Ġpredictions Ġand Ġtrain Ġmore Ġquickly Ġwithout Ġthe Ġgradual Ġunf ree zing Ġrequired Ġby ĠU LM Fi T . ĠWe Ġhave Ġopen - s ourced Ġall Ġcode Ġused Ġin Ġthis Ġcontest Ġto Ġfurther Ġenable Ġresearch Ġon Ġthis Ġtask Ġin Ġthe Ġfuture . </s> Author ĠContributions </s> BF Ġwas Ġthe Ġprimary Ġresearcher . ĠPC Ġcontributed Ġwith Ġsuggestions Ġfor Ġthe Ġrandom Ġseeds Ġas Ġa Ġhyper - param eters Ġand Ġlabel Ġsmoot hing Ġto Ġspeed Ġup Ġtraining . ĠJ H Ġcontributed Ġwith Ġsuggestion Ġfor Ġhigher Ġdrop out Ġthroughout Ġthe Ġnetwork Ġfor Ġmore Ġgeneral ization . </s> Acknowled gements </s> The Ġauthor Ġwould Ġlike Ġto Ġthank Ġall Ġthe Ġparticipants Ġon Ġthe Ġfast . ai Ġforums Ġfor Ġtheir Ġideas Ġand Ġsuggestions . ĠAlso , ĠKyle ĠK ast ner Ġfor Ġhis Ġedits , Ġsuggestions Ġand Ġrecommendations Ġin Ġwriting Ġup Ġthese Ġresults .