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FLOAT SELECTED: Table 2: Official shared task test set results.
1809.01541
Introduction </s> This Ġpaper Ġdescribes Ġour Ġapproach Ġand Ġresults Ġfor ĠTask Ġ2 Ġof Ġthe ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 Ġshared Ġtask Ġon Ġuniversal Ġmorph ological Ġrein f lection ĠB IB REF 0 Ġ. ĠThe Ġtask Ġis Ġto Ġgenerate Ġan Ġinf lected Ġword Ġform Ġgiven Ġits Ġle mma Ġand Ġthe Ġcontext Ġin Ġwhich Ġit Ġoccurs . </s> M orph ological Ġ( re ) inf lection Ġfrom Ġcontext Ġis Ġof Ġparticular Ġrelevance Ġto Ġthe Ġfield Ġof Ġcomputational Ġlingu istics : Ġit Ġis Ġcompelling Ġto Ġestimate Ġhow Ġwell Ġa Ġmachine - learn ed Ġsystem Ġcan Ġcapture Ġthe Ġmorph os ynt actic Ġproperties Ġof Ġa Ġword Ġgiven Ġits Ġcontext , Ġand Ġmap Ġthose Ġproperties Ġto Ġthe Ġcorrect Ġsurface Ġform Ġfor Ġa Ġgiven Ġle mma . </s> There Ġare Ġtwo Ġtracks Ġof ĠTask Ġ2 Ġof ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 : Ġin ĠTrack Ġ1 Ġthe Ġcontext Ġis Ġgiven Ġin Ġterms Ġof Ġword Ġforms , Ġle mm as Ġand Ġmorph os ynt actic Ġdescriptions Ġ( MS D ); Ġin ĠTrack Ġ2 Ġonly Ġword Ġforms Ġare Ġavailable . ĠSee ĠTable ĠT AB REF 1 Ġfor Ġan Ġexample . ĠTask Ġ2 Ġis Ġadditionally Ġsplit Ġin Ġthree Ġsettings Ġbased Ġon Ġdata Ġsize : Ġhigh , Ġmedium Ġand Ġlow , Ġwith Ġhigh - resource Ġdatasets Ġconsisting Ġof Ġup Ġto Ġ70 K Ġinstances Ġper Ġlanguage , Ġand Ġlow - resource Ġdatasets Ġconsisting Ġof Ġonly Ġabout Ġ1 K Ġinstances . </s> The Ġbaseline Ġprovided Ġby Ġthe Ġshared Ġtask Ġorganisers Ġis Ġa Ġseq 2 seq Ġmodel Ġwith Ġattention Ġ( similar Ġto Ġthe Ġwinning Ġsystem Ġfor Ġrein f lection Ġin ĠCo N LL âĢĵ S IG M OR PH ON Ġ2016 , ĠB IB REF 1 Ġ), Ġwhich Ġreceives Ġinformation Ġabout Ġcontext Ġthrough Ġan Ġembed ding Ġof Ġthe Ġtwo Ġwords Ġimmediately Ġadjacent Ġto Ġthe Ġtarget Ġform . ĠWe Ġuse Ġthis Ġbaseline Ġimplementation Ġas Ġa Ġstarting Ġpoint Ġand Ġachieve Ġthe Ġbest Ġoverall Ġaccuracy Ġof Ġ49 . 87 Ġon ĠTask Ġ2 Ġby Ġintroducing Ġthree Ġaugment ations Ġto Ġthe Ġprovided Ġbaseline Ġsystem : Ġ( 1 ) ĠWe Ġuse Ġan ĠL ST M Ġto Ġencode Ġthe Ġentire Ġavailable Ġcontext ; Ġ( 2 ) ĠWe Ġemploy Ġa Ġmulti - task Ġlearning Ġapproach Ġwith Ġthe Ġauxiliary Ġobjective Ġof ĠMS D Ġprediction ; Ġand Ġ( 3 ) ĠWe Ġtrain Ġthe Ġauxiliary Ġcomponent Ġin Ġa Ġmult ilingual Ġfashion , Ġover Ġsets Ġof Ġtwo Ġto Ġthree Ġlanguages . </s> In Ġanalys ing Ġthe Ġperformance Ġof Ġour Ġsystem , Ġwe Ġfound Ġthat Ġencoding Ġthe Ġfull Ġcontext Ġimproves Ġperformance Ġconsiderably Ġfor Ġall Ġlanguages : Ġ11 . 15 Ġpercentage Ġpoints Ġon Ġaverage , Ġalthough Ġit Ġalso Ġhighly Ġincreases Ġthe Ġvariance Ġin Ġresults . ĠMulti - task Ġlearning , Ġpaired Ġwith Ġmult ilingual Ġtraining Ġand Ġsubsequent Ġmon oling ual Ġfin et uning , Ġscored Ġhighest Ġfor Ġfive Ġout Ġof Ġseven Ġlanguages , Ġimproving Ġaccuracy Ġby Ġanother Ġ9 . 86 % Ġon Ġaverage . </s> System ĠDescription </s> Our Ġsystem Ġis Ġa Ġmodification Ġof Ġthe Ġprovided ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 Ġbaseline Ġsystem , Ġso Ġwe Ġbegin Ġthis Ġsection Ġwith Ġa Ġreiter ation Ġof Ġthe Ġbaseline Ġsystem Ġarchitecture , Ġfollowed Ġby Ġa Ġdescription Ġof Ġthe Ġthree Ġaugment ations Ġwe Ġintroduce . </s> Bas eline </s> The ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 Ġbaseline Ġis Ġdescribed Ġas Ġfollows : </s> The Ġsystem Ġis Ġan Ġenc oder - dec oder Ġon Ġcharacter Ġsequences . ĠIt Ġtakes Ġa Ġle mma Ġas Ġinput Ġand Ġgenerates Ġa Ġword Ġform . ĠThe Ġprocess Ġis Ġconditioned Ġon Ġthe Ġcontext Ġof Ġthe Ġle mma Ġ[...] ĠThe Ġbaseline Ġtreats Ġthe Ġle mma , Ġword Ġform Ġand ĠMS D Ġof Ġthe Ġprevious Ġand Ġfollowing Ġword Ġas Ġcontext Ġin Ġtrack Ġ1 . ĠIn Ġtrack Ġ2 , Ġthe Ġbaseline Ġonly Ġconsiders Ġthe Ġword Ġforms Ġof Ġthe Ġprevious Ġand Ġnext Ġword . Ġ[...] ĠThe Ġbaseline Ġsystem Ġconc aten ates Ġembed d ings Ġfor Ġcontext Ġword Ġforms , Ġle mm as Ġand ĠMS Ds Ġinto Ġa Ġcontext Ġvector . ĠThe Ġbaseline Ġthen Ġcomp utes Ġcharacter Ġembed d ings Ġfor Ġeach Ġcharacter Ġin Ġthe Ġinput Ġle mma . ĠEach Ġof Ġthese Ġis Ġconc aten ated Ġwith Ġa Ġcopy Ġof Ġthe Ġcontext Ġvector . ĠThe Ġresulting Ġsequence Ġof Ġvectors Ġis Ġencoded Ġusing Ġan ĠL ST M Ġenc oder . ĠSub sequently , Ġan ĠL ST M Ġdec oder Ġgenerates Ġthe Ġcharacters Ġin Ġthe Ġoutput Ġword Ġform Ġusing Ġenc oder Ġstates Ġand Ġan Ġattention Ġmechanism . </s> To Ġthat Ġwe Ġadd Ġa Ġfew Ġdetails Ġregarding Ġmodel Ġsize Ġand Ġtraining Ġschedule : </s> the Ġnumber Ġof ĠL ST M Ġlayers Ġis Ġone ; </s> embed ding Ġsize , ĠL ST M Ġlayer Ġsize Ġand Ġattention Ġlayer Ġsize Ġis Ġ100 ; </s> models Ġare Ġtrained Ġfor Ġ20 Ġepoch s ; </s> on Ġevery Ġepoch , Ġtraining Ġdata Ġis Ġsubs am pled Ġat Ġa Ġrate Ġof Ġ0 . 3 ; </s> L ST M Ġdrop out Ġis Ġapplied Ġat Ġa Ġrate Ġ0 . 3 ; </s> context Ġword Ġforms Ġare Ġrandomly Ġdropped Ġat Ġa Ġrate Ġof Ġ0 . 1 ; </s> the ĠAdam Ġoptim iser Ġis Ġused , Ġwith Ġa Ġdefault Ġlearning Ġrate Ġof Ġ0 . 001 ; Ġand </s> trained Ġmodels Ġare Ġevaluated Ġon Ġthe Ġdevelopment Ġdata Ġ( the Ġdata Ġfor Ġthe Ġshared Ġtask Ġcomes Ġalready Ġsplit Ġin Ġtrain Ġand Ġdev Ġsets ). </s> Our Ġsystem </s> Here Ġwe Ġcompare Ġand Ġcontrast Ġour Ġsystem Ġto Ġthe Ġbaseline Ġsystem . ĠA Ġdiagram Ġof Ġour Ġsystem Ġis Ġshown Ġin ĠFigure ĠFIG REF 4 Ġ. </s> The Ġidea Ġbehind Ġthis Ġmodification Ġis Ġto Ġprovide Ġthe Ġenc oder Ġwith Ġaccess Ġto Ġall Ġmorph o - sy nt actic Ġcues Ġpresent Ġin Ġthe Ġsentence . ĠIn Ġcontrast Ġto Ġthe Ġbaseline , Ġwhich Ġonly Ġenc odes Ġthe Ġimmediately Ġadjacent Ġcontext Ġof Ġa Ġtarget Ġword , Ġwe Ġencode Ġthe Ġentire Ġcontext . ĠAll Ġcontext Ġword Ġforms , Ġle mm as , Ġand ĠMS D Ġtags Ġ( in ĠTrack Ġ1 ) Ġare Ġembedded Ġin Ġtheir Ġrespective Ġhigh - dimensional Ġspaces Ġas Ġbefore , Ġand Ġtheir Ġembed d ings Ġare Ġconc aten ated . ĠHowever , Ġwe Ġnow Ġreduce Ġthe Ġentire Ġpast Ġcontext Ġto Ġa Ġfixed - size Ġvector Ġby Ġencoding Ġit Ġwith Ġa Ġforward ĠL ST M , Ġand Ġwe Ġsimilarly Ġrepresent Ġthe Ġfuture Ġcontext Ġby Ġencoding Ġit Ġwith Ġa Ġbackwards ĠL ST M . </s> We Ġintroduce Ġan Ġauxiliary Ġobjective Ġthat Ġis Ġmeant Ġto Ġincrease Ġthe Ġmorph o - sy nt actic Ġawareness Ġof Ġthe Ġenc oder Ġand Ġto Ġregular ise Ġthe Ġlearning Ġprocess âĢĶ the Ġtask Ġis Ġto Ġpredict Ġthe ĠMS D Ġtag Ġof Ġthe Ġtarget Ġform . ĠMS D Ġtag Ġpredictions Ġare Ġconditioned Ġon Ġthe Ġcontext Ġencoding , Ġas Ġdescribed Ġin ĠUID 15 Ġ. ĠTags Ġare Ġgenerated Ġwith Ġan ĠL ST M Ġone Ġcomponent Ġat Ġa Ġtime , Ġe . g . Ġthe Ġtag ĠPRO ; N OM ; SG ; 1 Ġis Ġpredicted Ġas Ġa Ġsequence Ġof Ġfour Ġcomponents , ĠIN LINE FORM 0 ĠPRO , ĠN OM , ĠSG , Ġ1 ĠIN LINE FORM 1 Ġ. </s> For Ġevery Ġtraining Ġinstance , Ġwe Ġback prop agate Ġthe Ġsum Ġof Ġthe Ġmain Ġloss Ġand Ġthe Ġauxiliary Ġloss Ġwithout Ġany Ġweight ing . </s> As ĠMS D Ġtags Ġare Ġonly Ġavailable Ġin ĠTrack Ġ1 , Ġthis Ġaug mentation Ġonly Ġapplies Ġto Ġthis Ġtrack . </s> The Ġparameters Ġof Ġthe Ġentire ĠMS D Ġ( aux iliary - task ) Ġdec oder Ġare Ġshared Ġacross Ġlanguages . </s> Since Ġa Ġgrouping Ġof Ġthe Ġlanguages Ġbased Ġon Ġlanguage Ġfamily Ġwould Ġhave Ġleft Ġseveral Ġlanguages Ġin Ġsingle - member Ġgroups Ġ( e . g . ĠRussian Ġis Ġthe Ġsole Ġrepresentative Ġof Ġthe ĠSlav ic Ġfamily ), Ġwe Ġexperiment Ġwith Ġrandom Ġgroup ings Ġof Ġtwo Ġto Ġthree Ġlanguages . ĠMult ilingual Ġtraining Ġis Ġperformed Ġby Ġrandomly Ġalternating Ġbetween Ġlanguages Ġfor Ġevery Ġnew Ġmin ib atch . ĠWe Ġdo Ġnot Ġpass Ġany Ġinformation Ġto Ġthe Ġauxiliary Ġdec oder Ġas Ġto Ġthe Ġsource Ġlanguage Ġof Ġthe Ġsignal Ġit Ġis Ġreceiving , Ġas Ġwe Ġassume Ġabstract Ġmorph o - sy nt actic Ġfeatures Ġare Ġshared Ġacross Ġlanguages . </s> After Ġ20 Ġepoch s Ġof Ġmult ilingual Ġtraining , Ġwe Ġperform Ġ5 Ġepoch s Ġof Ġmon oling ual Ġfin et uning Ġfor Ġeach Ġlanguage . ĠFor Ġthis Ġphase , Ġwe Ġreduce Ġthe Ġlearning Ġrate Ġto Ġa Ġtenth Ġof Ġthe Ġoriginal Ġlearning Ġrate , Ġi . e . Ġ0 . 0001 , Ġto Ġensure Ġthat Ġthe Ġmodels Ġare Ġindeed Ġbeing Ġfin et uned Ġrather Ġthan Ġret rained . </s> We Ġkeep Ġall Ġhyper param eters Ġthe Ġsame Ġas Ġin Ġthe Ġbaseline . ĠTraining Ġdata Ġis Ġsplit Ġ90 : 10 Ġfor Ġtraining Ġand Ġvalidation . ĠWe Ġtrain Ġour Ġmodels Ġfor Ġ50 Ġepoch s , Ġadding Ġearly Ġstopping Ġwith Ġa Ġtolerance Ġof Ġfive Ġepoch s Ġof Ġno Ġimprovement Ġin Ġthe Ġvalidation Ġloss . ĠWe Ġdo Ġnot Ġsubs ample Ġfrom Ġthe Ġtraining Ġdata . </s> We Ġtrain Ġmodels Ġfor Ġ50 Ġdifferent Ġrandom Ġcombinations Ġof Ġtwo Ġto Ġthree Ġlanguages Ġin ĠTrack Ġ1 , Ġand Ġ50 Ġmon oling ual Ġmodels Ġfor Ġeach Ġlanguage Ġin ĠTrack Ġ2 . ĠInstead Ġof Ġpicking Ġthe Ġsingle Ġmodel Ġthat Ġperforms Ġbest Ġon Ġthe Ġdevelopment Ġset Ġand Ġthus Ġrisking Ġto Ġselect Ġa Ġmodel Ġthat Ġhighly Ġover fits Ġthat Ġdata , Ġwe Ġuse Ġan Ġensemble Ġof Ġthe Ġfive Ġbest Ġmodels , Ġand Ġmake Ġthe Ġfinal Ġprediction Ġfor Ġa Ġgiven Ġtarget Ġform Ġwith Ġa Ġmajority Ġvote Ġover Ġthe Ġfive Ġpredictions . </s> Results Ġand ĠDiscussion </s> Test Ġresults Ġare Ġlisted Ġin ĠTable ĠT AB REF 17 Ġ. ĠOur Ġsystem Ġoutper forms Ġthe Ġbaseline Ġfor Ġall Ġsettings Ġand Ġlanguages Ġin ĠTrack Ġ1 Ġand Ġfor Ġalmost Ġall Ġin ĠTrack Ġ2 âĢĶ only Ġin Ġthe Ġhigh Ġresource Ġsetting Ġis Ġour Ġsystem Ġnot Ġdefinitively Ġsuperior Ġto Ġthe Ġbaseline . </s> Interestingly , Ġour Ġresults Ġin Ġthe Ġlow Ġresource Ġsetting Ġare Ġoften Ġhigher Ġfor ĠTrack Ġ2 Ġthan Ġfor ĠTrack Ġ1 , Ġeven Ġthough Ġcontextual Ġinformation Ġis Ġless Ġexplicit Ġin Ġthe ĠTrack Ġ2 Ġdata Ġand Ġthe Ġmult ilingual Ġmulti - t asking Ġapproach Ġdoes Ġnot Ġapply Ġto Ġthis Ġtrack . ĠWe Ġinterpret Ġthis Ġfinding Ġas Ġan Ġindicator Ġthat Ġa Ġsimpler Ġmodel Ġwith Ġfewer Ġparameters Ġworks Ġbetter Ġin Ġa Ġsetting Ġof Ġlimited Ġtraining Ġdata . ĠNevertheless , Ġwe Ġfocus Ġon Ġthe Ġlow Ġresource Ġsetting Ġin Ġthe Ġanalysis Ġbelow Ġdue Ġto Ġtime Ġlimitations . ĠAs Ġour ĠTrack Ġ1 Ġresults Ġare Ġstill Ġsubstantially Ġhigher Ġthan Ġthe Ġbaseline Ġresults , Ġwe Ġconsider Ġthis Ġanalysis Ġvalid Ġand Ġinsightful . </s> A bl ation ĠStudy </s> We Ġanalyse Ġthe Ġincremental Ġeffect Ġof Ġthe Ġdifferent Ġfeatures Ġin Ġour Ġsystem , Ġfocusing Ġon Ġthe Ġlow - resource Ġsetting Ġin ĠTrack Ġ1 Ġand Ġusing Ġdevelopment Ġdata . </s> Enc oding Ġthe Ġentire Ġcontext Ġwith Ġan ĠL ST M Ġhighly Ġincreases Ġthe Ġvariance Ġof Ġthe Ġobserved Ġresults . ĠSo Ġwe Ġtrained Ġfifty Ġmodels Ġfor Ġeach Ġlanguage Ġand Ġeach Ġarchitecture . ĠFigure ĠFIG REF 23 Ġvisual ises Ġthe Ġmeans Ġand Ġstandard Ġdeviations Ġover Ġthe Ġtrained Ġmodels . ĠIn Ġaddition , Ġwe Ġvisual ise Ġthe Ġaverage Ġaccuracy Ġfor Ġthe Ġfive Ġbest Ġmodels Ġfor Ġeach Ġlanguage Ġand Ġarchitecture , Ġas Ġthese Ġare Ġthe Ġmodels Ġwe Ġuse Ġin Ġthe Ġfinal Ġensemble Ġprediction . ĠBelow Ġwe Ġrefer Ġto Ġthese Ġnumbers Ġonly . </s> The Ġresults Ġindicate Ġthat Ġencoding Ġthe Ġfull Ġcontext Ġwith Ġan ĠL ST M Ġhighly Ġenhances Ġthe Ġperformance Ġof Ġthe Ġmodel , Ġby Ġ11 . 15 % Ġon Ġaverage . ĠThis Ġobservation Ġexplains Ġthe Ġhigh Ġresults Ġwe Ġobtain Ġalso Ġfor ĠTrack Ġ2 . </s> Adding Ġthe Ġauxiliary Ġobjective Ġof ĠMS D Ġprediction Ġhas Ġa Ġvariable Ġeffect : Ġfor Ġfour Ġlanguages Ġ( de , Ġen , Ġes , Ġand Ġsv ) Ġthe Ġeffect Ġis Ġpositive , Ġwhile Ġfor Ġthe Ġrest Ġit Ġis Ġnegative . ĠWe Ġconsider Ġthis Ġto Ġbe Ġan Ġissue Ġof Ġinsufficient Ġdata Ġfor Ġthe Ġtraining Ġof Ġthe Ġauxiliary Ġcomponent Ġin Ġthe Ġlow Ġresource Ġsetting Ġwe Ġare Ġworking Ġwith . </s> We Ġindeed Ġsee Ġresults Ġimproving Ġdrastically Ġwith Ġthe Ġintroduction Ġof Ġmult ilingual Ġtraining , Ġwith Ġmult ilingual Ġresults Ġbeing Ġ7 . 96 % Ġhigher Ġthan Ġmon oling ual Ġones Ġon Ġaverage . </s> We Ġstudied Ġthe Ġfive Ġbest Ġmodels Ġfor Ġeach Ġlanguage Ġas Ġemerging Ġfrom Ġthe Ġmult ilingual Ġtraining Ġ( listed Ġin ĠTable ĠT AB REF 27 Ġ) Ġand Ġfound Ġno Ġstrong Ġlinguistic Ġpatterns . ĠThe Ġen âĢĵ sv Ġpairing Ġseems Ġto Ġyield Ġgood Ġmodels Ġfor Ġthese Ġlanguages , Ġwhich Ġcould Ġbe Ġexplained Ġin Ġterms Ġof Ġtheir Ġcommon Ġlanguage Ġfamily Ġand Ġsimilar Ġmorphology . ĠThe Ġother Ġnatural Ġpair ings , Ġhowever , Ġfr âĢĵ es , Ġand Ġde âĢĵ sv , Ġare Ġnot Ġso Ġfrequent Ġamong Ġthe Ġbest Ġmodels Ġfor Ġthese Ġpairs Ġof Ġlanguages . </s> Finally , Ġmon oling ual Ġfin et uning Ġimproves Ġaccuracy Ġacross Ġthe Ġboard , Ġas Ġone Ġwould Ġexpect , Ġby Ġ2 . 72 % Ġon Ġaverage . </s> The Ġfinal Ġobservation Ġto Ġbe Ġmade Ġbased Ġon Ġthis Ġbreakdown Ġof Ġresults Ġis Ġthat Ġthe Ġmulti - t asking Ġapproach Ġpaired Ġwith Ġmult ilingual Ġtraining Ġand Ġsubsequent Ġmon oling ual Ġfin et uning Ġoutper forms Ġthe Ġother Ġarchitectures Ġfor Ġfive Ġout Ġof Ġseven Ġlanguages : Ġde , Ġen , Ġfr , Ġru Ġand Ġsv . ĠFor Ġthe Ġother Ġtwo Ġlanguages Ġin Ġthe Ġdataset , Ġes Ġand Ġfi , Ġthe Ġdifference Ġbetween Ġthis Ġapproach Ġand Ġthe Ġapproach Ġthat Ġemerged Ġas Ġbest Ġfor Ġthem Ġis Ġless Ġthan Ġ1 %. ĠThe Ġoverall Ġimprovement Ġof Ġthe Ġmult ilingual Ġmulti - t asking Ġapproach Ġover Ġthe Ġbaseline Ġis Ġ18 . 30 %. </s> Error Ġanalysis </s> Here Ġwe Ġstudy Ġthe Ġerrors Ġproduced Ġby Ġour Ġsystem Ġon Ġthe ĠEnglish Ġtest Ġset Ġto Ġbetter Ġunderstand Ġthe Ġremaining Ġshortcomings Ġof Ġthe Ġapproach . ĠA Ġsmall Ġportion Ġof Ġthe Ġwrong Ġpredictions Ġpoint Ġto Ġan Ġincorrect Ġinterpretation Ġof Ġthe Ġmorph o - sy nt actic Ġconditioning Ġof Ġthe Ġcontext , Ġe . g . Ġthe Ġsystem Ġpredicted Ġplan Ġinstead Ġof Ġplans Ġin Ġthe Ġcontext ĠOur Ġ_ Ġinclude Ġraising Ġprivate Ġcapital . ĠThe Ġmajority Ġof Ġwrong Ġpredictions , Ġhowever , Ġare Ġnonsensical , Ġlike Ġbomb Ġfor Ġjob , Ġf ify Ġfor Ġfixing , Ġand Ġg nder rate Ġfor Ġunderstand . ĠThis Ġobservation Ġsuggests Ġthat Ġgenerally Ġthe Ġsystem Ġdid Ġnot Ġlearn Ġto Ġcopy Ġthe Ġcharacters Ġof Ġle mma Ġinto Ġinf lected Ġform , Ġwhich Ġis Ġall Ġit Ġneeds Ġto Ġdo Ġin Ġa Ġlarge Ġnumber Ġof Ġcases . ĠThis Ġissue Ġcould Ġbe Ġallev iated Ġwith Ġsimple Ġdata Ġaug mentation Ġtechniques Ġthat Ġencourage Ġaut oen c oding ĠB IB REF 2 Ġ. </s> MS D Ġprediction </s> Figure ĠFIG REF 32 Ġsummar ises Ġthe Ġaverage ĠMS D - pred iction Ġaccuracy Ġfor Ġthe Ġmulti - t asking Ġexperiments Ġdiscussed Ġabove . ĠAccuracy Ġhere Ġis Ġgenerally Ġhigher Ġthan Ġon Ġthe Ġmain Ġtask , Ġwith Ġthe Ġmult ilingual Ġfin et uned Ġsetup Ġfor ĠSpanish Ġand Ġthe Ġmon oling ual Ġsetup Ġfor ĠFrench Ġscoring Ġbest : Ġ66 . 59 % Ġand Ġ65 . 35 %, Ġrespectively . ĠThis Ġobservation Ġillustrates Ġthe Ġadded Ġdifficulty Ġof Ġgenerating Ġthe Ġcorrect Ġsurface Ġform Ġeven Ġwhen Ġthe Ġmorph os ynt actic Ġdescription Ġhas Ġbeen Ġidentified Ġcorrectly . </s> We Ġobserve Ġsome Ġcorrelation Ġbetween Ġthese Ġnumbers Ġand Ġaccuracy Ġon Ġthe Ġmain Ġtask : Ġfor Ġde , Ġen , Ġru Ġand Ġsv , Ġthe Ġbrown , Ġpink Ġand Ġblue Ġbars Ġhere Ġpattern Ġin Ġthe Ġsame Ġway Ġas Ġthe Ġcorresponding ĠIN LINE FORM 0 Ġ' s Ġin ĠFigure ĠFIG REF 23 Ġ. ĠOne Ġnotable Ġexception Ġto Ġthis Ġpattern Ġis Ġfr Ġwhere Ġinf lection Ġgains Ġa Ġlot Ġfrom Ġmult ilingual Ġtraining , Ġwhile ĠMS D Ġprediction Ġsuffers Ġgreatly . ĠNotice Ġthat Ġthe Ġmagnitude Ġof Ġchange Ġis Ġnot Ġalways Ġthe Ġsame , Ġhowever , Ġeven Ġwhen Ġthe Ġgeneral Ġdirection Ġmatches : Ġfor Ġru , Ġfor Ġexample , Ġmult ilingual Ġtraining Ġbenefits Ġinf lection Ġmuch Ġmore Ġthan Ġin Ġbenefits ĠMS D Ġprediction , Ġeven Ġthough Ġthe ĠMS D Ġdec oder Ġis Ġthe Ġonly Ġcomponent Ġthat Ġis Ġactually Ġshared Ġbetween Ġlanguages . ĠThis Ġobservation Ġillustrates Ġthe Ġtwo - fold Ġeffect Ġof Ġmulti - task Ġtraining : Ġan Ġauxiliary Ġtask Ġcan Ġeither Ġinform Ġthe Ġmain Ġtask Ġthrough Ġthe Ġparameters Ġthe Ġtwo Ġtasks Ġshare , Ġor Ġit Ġcan Ġhelp Ġthe Ġmain Ġtask Ġlearning Ġthrough Ġits Ġregular ising Ġeffect . </s> Related ĠWork </s> Our Ġsystem Ġis Ġinspired Ġby Ġprevious Ġwork Ġon Ġmulti - task Ġlearning Ġand Ġmulti - ling ual Ġlearning , Ġmainly Ġbuilding Ġon Ġtwo Ġintu itions : Ġ( 1 ) Ġjointly Ġlearning Ġrelated Ġtasks Ġtends Ġto Ġbe Ġbeneficial ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġ, ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ; Ġand Ġ( 2 ) Ġjointly Ġlearning Ġrelated Ġlanguages Ġin Ġan ĠM TL - inspired Ġframework Ġtends Ġto Ġbe Ġbeneficial ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ, ĠB IB REF 10 Ġ. ĠIn Ġthe Ġcontext Ġof Ġcomputational Ġmorphology , Ġmulti - ling ual Ġapproaches Ġhave Ġpreviously Ġbeen Ġemployed Ġfor Ġmorph ological Ġrein f lection ĠB IB REF 2 Ġand Ġfor Ġparadigm Ġcompletion ĠB IB REF 11 Ġ. ĠIn Ġboth Ġof Ġthese Ġcases , Ġhowever , Ġthe Ġavailable Ġdatasets Ġcovered Ġmore Ġlanguages , Ġ40 Ġand Ġ21 , Ġrespectively , Ġwhich Ġallowed Ġfor Ġlingu istically - mot ivated Ġlanguage Ġgroup ings Ġand Ġfor Ġparameter Ġsharing Ġdirectly Ġon Ġthe Ġlevel Ġof Ġcharacters . ĠB IB REF 10 Ġexplore Ġparameter Ġsharing Ġbetween Ġrelated Ġlanguages Ġfor Ġdependency Ġparsing , Ġand Ġfind Ġthat Ġsharing Ġis Ġmore Ġbeneficial Ġin Ġthe Ġcase Ġof Ġclosely Ġrelated Ġlanguages . </s> Con clusions </s> In Ġthis Ġpaper Ġwe Ġdescribed Ġour Ġsystem Ġfor Ġthe ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 Ġshared Ġtask Ġon ĠUniversal ĠMorph ological ĠRe inf lection , ĠTask Ġ2 , Ġwhich Ġachieved Ġthe Ġbest Ġperformance Ġout Ġof Ġall Ġsystems Ġsubmitted , Ġan Ġoverall Ġaccuracy Ġof Ġ49 . 87 . ĠWe Ġshowed Ġin Ġan Ġab lation Ġstudy Ġthat Ġthis Ġis Ġdue Ġto Ġthree Ġcore Ġinnovations , Ġwhich Ġextend Ġa Ġcharacter - based Ġenc oder - dec oder Ġmodel : Ġ( 1 ) Ġa Ġwide Ġcontext Ġwindow , Ġencoding Ġthe Ġentire Ġavailable Ġcontext ; Ġ( 2 ) Ġmulti - task Ġlearning Ġwith Ġthe Ġauxiliary Ġtask Ġof ĠMS D Ġprediction , Ġwhich Ġacts Ġas Ġa Ġregular iser ; Ġ( 3 ) Ġa Ġmult ilingual Ġapproach , Ġexploiting Ġinformation Ġacross Ġlanguages . ĠIn Ġfuture Ġwork Ġwe Ġaim Ġto Ġgain Ġbetter Ġunderstanding Ġof Ġthe Ġincrease Ġin Ġvariance Ġof Ġthe Ġresults Ġintroduced Ġby Ġeach Ġof Ġour Ġmodifications Ġand Ġthe Ġreasons Ġfor Ġthe Ġvarying Ġeffect Ġof Ġmulti - task Ġlearning Ġfor Ġdifferent Ġlanguages . </s> Acknowled gements </s> We Ġgrate fully Ġacknowledge Ġthe Ġsupport Ġof Ġthe ĠNVIDIA ĠCorporation Ġwith Ġthe Ġdonation Ġof Ġthe ĠTitan ĠX p ĠGPU Ġused Ġfor Ġthis Ġresearch .
Introduction This paper describes our approach and results for Task 2 of the CoNLL–SIGMORPHON 2018 shared task on universal morphological reinflection BIBREF0 . The task is to generate an inflected word form given its lemma and the context in which it occurs. Morphological (re)inflection from context is of particular relevance to the field of computational linguistics: it is compelling to estimate how well a machine-learned system can capture the morphosyntactic properties of a word given its context, and map those properties to the correct surface form for a given lemma. There are two tracks of Task 2 of CoNLL–SIGMORPHON 2018: in Track 1 the context is given in terms of word forms, lemmas and morphosyntactic descriptions (MSD); in Track 2 only word forms are available. See Table TABREF1 for an example. Task 2 is additionally split in three settings based on data size: high, medium and low, with high-resource datasets consisting of up to 70K instances per language, and low-resource datasets consisting of only about 1K instances. The baseline provided by the shared task organisers is a seq2seq model with attention (similar to the winning system for reinflection in CoNLL–SIGMORPHON 2016, BIBREF1 ), which receives information about context through an embedding of the two words immediately adjacent to the target form. We use this baseline implementation as a starting point and achieve the best overall accuracy of 49.87 on Task 2 by introducing three augmentations to the provided baseline system: (1) We use an LSTM to encode the entire available context; (2) We employ a multi-task learning approach with the auxiliary objective of MSD prediction; and (3) We train the auxiliary component in a multilingual fashion, over sets of two to three languages. In analysing the performance of our system, we found that encoding the full context improves performance considerably for all languages: 11.15 percentage points on average, although it also highly increases the variance in results. Multi-task learning, paired with multilingual training and subsequent monolingual finetuning, scored highest for five out of seven languages, improving accuracy by another 9.86% on average. System Description Our system is a modification of the provided CoNLL–SIGMORPHON 2018 baseline system, so we begin this section with a reiteration of the baseline system architecture, followed by a description of the three augmentations we introduce. Baseline The CoNLL–SIGMORPHON 2018 baseline is described as follows: The system is an encoder-decoder on character sequences. It takes a lemma as input and generates a word form. The process is conditioned on the context of the lemma [...] The baseline treats the lemma, word form and MSD of the previous and following word as context in track 1. In track 2, the baseline only considers the word forms of the previous and next word. [...] The baseline system concatenates embeddings for context word forms, lemmas and MSDs into a context vector. The baseline then computes character embeddings for each character in the input lemma. Each of these is concatenated with a copy of the context vector. The resulting sequence of vectors is encoded using an LSTM encoder. Subsequently, an LSTM decoder generates the characters in the output word form using encoder states and an attention mechanism. To that we add a few details regarding model size and training schedule: the number of LSTM layers is one; embedding size, LSTM layer size and attention layer size is 100; models are trained for 20 epochs; on every epoch, training data is subsampled at a rate of 0.3; LSTM dropout is applied at a rate 0.3; context word forms are randomly dropped at a rate of 0.1; the Adam optimiser is used, with a default learning rate of 0.001; and trained models are evaluated on the development data (the data for the shared task comes already split in train and dev sets). Our system Here we compare and contrast our system to the baseline system. A diagram of our system is shown in Figure FIGREF4 . The idea behind this modification is to provide the encoder with access to all morpho-syntactic cues present in the sentence. In contrast to the baseline, which only encodes the immediately adjacent context of a target word, we encode the entire context. All context word forms, lemmas, and MSD tags (in Track 1) are embedded in their respective high-dimensional spaces as before, and their embeddings are concatenated. However, we now reduce the entire past context to a fixed-size vector by encoding it with a forward LSTM, and we similarly represent the future context by encoding it with a backwards LSTM. We introduce an auxiliary objective that is meant to increase the morpho-syntactic awareness of the encoder and to regularise the learning process—the task is to predict the MSD tag of the target form. MSD tag predictions are conditioned on the context encoding, as described in UID15 . Tags are generated with an LSTM one component at a time, e.g. the tag PRO;NOM;SG;1 is predicted as a sequence of four components, INLINEFORM0 PRO, NOM, SG, 1 INLINEFORM1 . For every training instance, we backpropagate the sum of the main loss and the auxiliary loss without any weighting. As MSD tags are only available in Track 1, this augmentation only applies to this track. The parameters of the entire MSD (auxiliary-task) decoder are shared across languages. Since a grouping of the languages based on language family would have left several languages in single-member groups (e.g. Russian is the sole representative of the Slavic family), we experiment with random groupings of two to three languages. Multilingual training is performed by randomly alternating between languages for every new minibatch. We do not pass any information to the auxiliary decoder as to the source language of the signal it is receiving, as we assume abstract morpho-syntactic features are shared across languages. After 20 epochs of multilingual training, we perform 5 epochs of monolingual finetuning for each language. For this phase, we reduce the learning rate to a tenth of the original learning rate, i.e. 0.0001, to ensure that the models are indeed being finetuned rather than retrained. We keep all hyperparameters the same as in the baseline. Training data is split 90:10 for training and validation. We train our models for 50 epochs, adding early stopping with a tolerance of five epochs of no improvement in the validation loss. We do not subsample from the training data. We train models for 50 different random combinations of two to three languages in Track 1, and 50 monolingual models for each language in Track 2. Instead of picking the single model that performs best on the development set and thus risking to select a model that highly overfits that data, we use an ensemble of the five best models, and make the final prediction for a given target form with a majority vote over the five predictions. Results and Discussion Test results are listed in Table TABREF17 . Our system outperforms the baseline for all settings and languages in Track 1 and for almost all in Track 2—only in the high resource setting is our system not definitively superior to the baseline. Interestingly, our results in the low resource setting are often higher for Track 2 than for Track 1, even though contextual information is less explicit in the Track 2 data and the multilingual multi-tasking approach does not apply to this track. We interpret this finding as an indicator that a simpler model with fewer parameters works better in a setting of limited training data. Nevertheless, we focus on the low resource setting in the analysis below due to time limitations. As our Track 1 results are still substantially higher than the baseline results, we consider this analysis valid and insightful. Ablation Study We analyse the incremental effect of the different features in our system, focusing on the low-resource setting in Track 1 and using development data. Encoding the entire context with an LSTM highly increases the variance of the observed results. So we trained fifty models for each language and each architecture. Figure FIGREF23 visualises the means and standard deviations over the trained models. In addition, we visualise the average accuracy for the five best models for each language and architecture, as these are the models we use in the final ensemble prediction. Below we refer to these numbers only. The results indicate that encoding the full context with an LSTM highly enhances the performance of the model, by 11.15% on average. This observation explains the high results we obtain also for Track 2. Adding the auxiliary objective of MSD prediction has a variable effect: for four languages (de, en, es, and sv) the effect is positive, while for the rest it is negative. We consider this to be an issue of insufficient data for the training of the auxiliary component in the low resource setting we are working with. We indeed see results improving drastically with the introduction of multilingual training, with multilingual results being 7.96% higher than monolingual ones on average. We studied the five best models for each language as emerging from the multilingual training (listed in Table TABREF27 ) and found no strong linguistic patterns. The en–sv pairing seems to yield good models for these languages, which could be explained in terms of their common language family and similar morphology. The other natural pairings, however, fr–es, and de–sv, are not so frequent among the best models for these pairs of languages. Finally, monolingual finetuning improves accuracy across the board, as one would expect, by 2.72% on average. The final observation to be made based on this breakdown of results is that the multi-tasking approach paired with multilingual training and subsequent monolingual finetuning outperforms the other architectures for five out of seven languages: de, en, fr, ru and sv. For the other two languages in the dataset, es and fi, the difference between this approach and the approach that emerged as best for them is less than 1%. The overall improvement of the multilingual multi-tasking approach over the baseline is 18.30%. Error analysis Here we study the errors produced by our system on the English test set to better understand the remaining shortcomings of the approach. A small portion of the wrong predictions point to an incorrect interpretation of the morpho-syntactic conditioning of the context, e.g. the system predicted plan instead of plans in the context Our _ include raising private capital. The majority of wrong predictions, however, are nonsensical, like bomb for job, fify for fixing, and gnderrate for understand. This observation suggests that generally the system did not learn to copy the characters of lemma into inflected form, which is all it needs to do in a large number of cases. This issue could be alleviated with simple data augmentation techniques that encourage autoencoding BIBREF2 . MSD prediction Figure FIGREF32 summarises the average MSD-prediction accuracy for the multi-tasking experiments discussed above. Accuracy here is generally higher than on the main task, with the multilingual finetuned setup for Spanish and the monolingual setup for French scoring best: 66.59% and 65.35%, respectively. This observation illustrates the added difficulty of generating the correct surface form even when the morphosyntactic description has been identified correctly. We observe some correlation between these numbers and accuracy on the main task: for de, en, ru and sv, the brown, pink and blue bars here pattern in the same way as the corresponding INLINEFORM0 's in Figure FIGREF23 . One notable exception to this pattern is fr where inflection gains a lot from multilingual training, while MSD prediction suffers greatly. Notice that the magnitude of change is not always the same, however, even when the general direction matches: for ru, for example, multilingual training benefits inflection much more than in benefits MSD prediction, even though the MSD decoder is the only component that is actually shared between languages. This observation illustrates the two-fold effect of multi-task training: an auxiliary task can either inform the main task through the parameters the two tasks share, or it can help the main task learning through its regularising effect. Related Work Our system is inspired by previous work on multi-task learning and multi-lingual learning, mainly building on two intuitions: (1) jointly learning related tasks tends to be beneficial BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 ; and (2) jointly learning related languages in an MTL-inspired framework tends to be beneficial BIBREF8 , BIBREF9 , BIBREF10 . In the context of computational morphology, multi-lingual approaches have previously been employed for morphological reinflection BIBREF2 and for paradigm completion BIBREF11 . In both of these cases, however, the available datasets covered more languages, 40 and 21, respectively, which allowed for linguistically-motivated language groupings and for parameter sharing directly on the level of characters. BIBREF10 explore parameter sharing between related languages for dependency parsing, and find that sharing is more beneficial in the case of closely related languages. Conclusions In this paper we described our system for the CoNLL–SIGMORPHON 2018 shared task on Universal Morphological Reinflection, Task 2, which achieved the best performance out of all systems submitted, an overall accuracy of 49.87. We showed in an ablation study that this is due to three core innovations, which extend a character-based encoder-decoder model: (1) a wide context window, encoding the entire available context; (2) multi-task learning with the auxiliary task of MSD prediction, which acts as a regulariser; (3) a multilingual approach, exploiting information across languages. In future work we aim to gain better understanding of the increase in variance of the results introduced by each of our modifications and the reasons for the varying effect of multi-task learning for different languages. Acknowledgements We gratefully acknowledge the support of the NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
What languages are evaluated?
c32adef59efcb9d1a5b10e1d7c999a825c9e6d9a
What Ġlanguages Ġare Ġevaluated ?
German , ĠEnglish , ĠSpanish , ĠFinnish , ĠFrench , ĠRussian , Ġ ĠSwedish .
<s> What Ġlanguages Ġare Ġevaluated ? </s> Introduction </s> This Ġpaper Ġdescribes Ġour Ġapproach Ġand Ġresults Ġfor ĠTask Ġ2 Ġof Ġthe ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 Ġshared Ġtask Ġon Ġuniversal Ġmorph ological Ġrein f lection ĠB IB REF 0 Ġ. ĠThe Ġtask Ġis Ġto Ġgenerate Ġan Ġinf lected Ġword Ġform Ġgiven Ġits Ġle mma Ġand Ġthe Ġcontext Ġin Ġwhich Ġit Ġoccurs . </s> M orph ological Ġ( re ) inf lection Ġfrom Ġcontext Ġis Ġof Ġparticular Ġrelevance Ġto Ġthe Ġfield Ġof Ġcomputational Ġlingu istics : Ġit Ġis Ġcompelling Ġto Ġestimate Ġhow Ġwell Ġa Ġmachine - learn ed Ġsystem Ġcan Ġcapture Ġthe Ġmorph os ynt actic Ġproperties Ġof Ġa Ġword Ġgiven Ġits Ġcontext , Ġand Ġmap Ġthose Ġproperties Ġto Ġthe Ġcorrect Ġsurface Ġform Ġfor Ġa Ġgiven Ġle mma . </s> There Ġare Ġtwo Ġtracks Ġof ĠTask Ġ2 Ġof ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 : Ġin ĠTrack Ġ1 Ġthe Ġcontext Ġis Ġgiven Ġin Ġterms Ġof Ġword Ġforms , Ġle mm as Ġand Ġmorph os ynt actic Ġdescriptions Ġ( MS D ); Ġin ĠTrack Ġ2 Ġonly Ġword Ġforms Ġare Ġavailable . ĠSee ĠTable ĠT AB REF 1 Ġfor Ġan Ġexample . ĠTask Ġ2 Ġis Ġadditionally Ġsplit Ġin Ġthree Ġsettings Ġbased Ġon Ġdata Ġsize : Ġhigh , Ġmedium Ġand Ġlow , Ġwith Ġhigh - resource Ġdatasets Ġconsisting Ġof Ġup Ġto Ġ70 K Ġinstances Ġper Ġlanguage , Ġand Ġlow - resource Ġdatasets Ġconsisting Ġof Ġonly Ġabout Ġ1 K Ġinstances . </s> The Ġbaseline Ġprovided Ġby Ġthe Ġshared Ġtask Ġorganisers Ġis Ġa Ġseq 2 seq Ġmodel Ġwith Ġattention Ġ( similar Ġto Ġthe Ġwinning Ġsystem Ġfor Ġrein f lection Ġin ĠCo N LL âĢĵ S IG M OR PH ON Ġ2016 , ĠB IB REF 1 Ġ), Ġwhich Ġreceives Ġinformation Ġabout Ġcontext Ġthrough Ġan Ġembed ding Ġof Ġthe Ġtwo Ġwords Ġimmediately Ġadjacent Ġto Ġthe Ġtarget Ġform . ĠWe Ġuse Ġthis Ġbaseline Ġimplementation Ġas Ġa Ġstarting Ġpoint Ġand Ġachieve Ġthe Ġbest Ġoverall Ġaccuracy Ġof Ġ49 . 87 Ġon ĠTask Ġ2 Ġby Ġintroducing Ġthree Ġaugment ations Ġto Ġthe Ġprovided Ġbaseline Ġsystem : Ġ( 1 ) ĠWe Ġuse Ġan ĠL ST M Ġto Ġencode Ġthe Ġentire Ġavailable Ġcontext ; Ġ( 2 ) ĠWe Ġemploy Ġa Ġmulti - task Ġlearning Ġapproach Ġwith Ġthe Ġauxiliary Ġobjective Ġof ĠMS D Ġprediction ; Ġand Ġ( 3 ) ĠWe Ġtrain Ġthe Ġauxiliary Ġcomponent Ġin Ġa Ġmult ilingual Ġfashion , Ġover Ġsets Ġof Ġtwo Ġto Ġthree Ġlanguages . </s> In Ġanalys ing Ġthe Ġperformance Ġof Ġour Ġsystem , Ġwe Ġfound Ġthat Ġencoding Ġthe Ġfull Ġcontext Ġimproves Ġperformance Ġconsiderably Ġfor Ġall Ġlanguages : Ġ11 . 15 Ġpercentage Ġpoints Ġon Ġaverage , Ġalthough Ġit Ġalso Ġhighly Ġincreases Ġthe Ġvariance Ġin Ġresults . ĠMulti - task Ġlearning , Ġpaired Ġwith Ġmult ilingual Ġtraining Ġand Ġsubsequent Ġmon oling ual Ġfin et uning , Ġscored Ġhighest Ġfor Ġfive Ġout Ġof Ġseven Ġlanguages , Ġimproving Ġaccuracy Ġby Ġanother Ġ9 . 86 % Ġon Ġaverage . </s> System ĠDescription </s> Our Ġsystem Ġis Ġa Ġmodification Ġof Ġthe Ġprovided ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 Ġbaseline Ġsystem , Ġso Ġwe Ġbegin Ġthis Ġsection Ġwith Ġa Ġreiter ation Ġof Ġthe Ġbaseline Ġsystem Ġarchitecture , Ġfollowed Ġby Ġa Ġdescription Ġof Ġthe Ġthree Ġaugment ations Ġwe Ġintroduce . </s> Bas eline </s> The ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 Ġbaseline Ġis Ġdescribed Ġas Ġfollows : </s> The Ġsystem Ġis Ġan Ġenc oder - dec oder Ġon Ġcharacter Ġsequences . ĠIt Ġtakes Ġa Ġle mma Ġas Ġinput Ġand Ġgenerates Ġa Ġword Ġform . ĠThe Ġprocess Ġis Ġconditioned Ġon Ġthe Ġcontext Ġof Ġthe Ġle mma Ġ[...] ĠThe Ġbaseline Ġtreats Ġthe Ġle mma , Ġword Ġform Ġand ĠMS D Ġof Ġthe Ġprevious Ġand Ġfollowing Ġword Ġas Ġcontext Ġin Ġtrack Ġ1 . ĠIn Ġtrack Ġ2 , Ġthe Ġbaseline Ġonly Ġconsiders Ġthe Ġword Ġforms Ġof Ġthe Ġprevious Ġand Ġnext Ġword . Ġ[...] ĠThe Ġbaseline Ġsystem Ġconc aten ates Ġembed d ings Ġfor Ġcontext Ġword Ġforms , Ġle mm as Ġand ĠMS Ds Ġinto Ġa Ġcontext Ġvector . ĠThe Ġbaseline Ġthen Ġcomp utes Ġcharacter Ġembed d ings Ġfor Ġeach Ġcharacter Ġin Ġthe Ġinput Ġle mma . ĠEach Ġof Ġthese Ġis Ġconc aten ated Ġwith Ġa Ġcopy Ġof Ġthe Ġcontext Ġvector . ĠThe Ġresulting Ġsequence Ġof Ġvectors Ġis Ġencoded Ġusing Ġan ĠL ST M Ġenc oder . ĠSub sequently , Ġan ĠL ST M Ġdec oder Ġgenerates Ġthe Ġcharacters Ġin Ġthe Ġoutput Ġword Ġform Ġusing Ġenc oder Ġstates Ġand Ġan Ġattention Ġmechanism . </s> To Ġthat Ġwe Ġadd Ġa Ġfew Ġdetails Ġregarding Ġmodel Ġsize Ġand Ġtraining Ġschedule : </s> the Ġnumber Ġof ĠL ST M Ġlayers Ġis Ġone ; </s> embed ding Ġsize , ĠL ST M Ġlayer Ġsize Ġand Ġattention Ġlayer Ġsize Ġis Ġ100 ; </s> models Ġare Ġtrained Ġfor Ġ20 Ġepoch s ; </s> on Ġevery Ġepoch , Ġtraining Ġdata Ġis Ġsubs am pled Ġat Ġa Ġrate Ġof Ġ0 . 3 ; </s> L ST M Ġdrop out Ġis Ġapplied Ġat Ġa Ġrate Ġ0 . 3 ; </s> context Ġword Ġforms Ġare Ġrandomly Ġdropped Ġat Ġa Ġrate Ġof Ġ0 . 1 ; </s> the ĠAdam Ġoptim iser Ġis Ġused , Ġwith Ġa Ġdefault Ġlearning Ġrate Ġof Ġ0 . 001 ; Ġand </s> trained Ġmodels Ġare Ġevaluated Ġon Ġthe Ġdevelopment Ġdata Ġ( the Ġdata Ġfor Ġthe Ġshared Ġtask Ġcomes Ġalready Ġsplit Ġin Ġtrain Ġand Ġdev Ġsets ). </s> Our Ġsystem </s> Here Ġwe Ġcompare Ġand Ġcontrast Ġour Ġsystem Ġto Ġthe Ġbaseline Ġsystem . ĠA Ġdiagram Ġof Ġour Ġsystem Ġis Ġshown Ġin ĠFigure ĠFIG REF 4 Ġ. </s> The Ġidea Ġbehind Ġthis Ġmodification Ġis Ġto Ġprovide Ġthe Ġenc oder Ġwith Ġaccess Ġto Ġall Ġmorph o - sy nt actic Ġcues Ġpresent Ġin Ġthe Ġsentence . ĠIn Ġcontrast Ġto Ġthe Ġbaseline , Ġwhich Ġonly Ġenc odes Ġthe Ġimmediately Ġadjacent Ġcontext Ġof Ġa Ġtarget Ġword , Ġwe Ġencode Ġthe Ġentire Ġcontext . ĠAll Ġcontext Ġword Ġforms , Ġle mm as , Ġand ĠMS D Ġtags Ġ( in ĠTrack Ġ1 ) Ġare Ġembedded Ġin Ġtheir Ġrespective Ġhigh - dimensional Ġspaces Ġas Ġbefore , Ġand Ġtheir Ġembed d ings Ġare Ġconc aten ated . ĠHowever , Ġwe Ġnow Ġreduce Ġthe Ġentire Ġpast Ġcontext Ġto Ġa Ġfixed - size Ġvector Ġby Ġencoding Ġit Ġwith Ġa Ġforward ĠL ST M , Ġand Ġwe Ġsimilarly Ġrepresent Ġthe Ġfuture Ġcontext Ġby Ġencoding Ġit Ġwith Ġa Ġbackwards ĠL ST M . </s> We Ġintroduce Ġan Ġauxiliary Ġobjective Ġthat Ġis Ġmeant Ġto Ġincrease Ġthe Ġmorph o - sy nt actic Ġawareness Ġof Ġthe Ġenc oder Ġand Ġto Ġregular ise Ġthe Ġlearning Ġprocess âĢĶ the Ġtask Ġis Ġto Ġpredict Ġthe ĠMS D Ġtag Ġof Ġthe Ġtarget Ġform . ĠMS D Ġtag Ġpredictions Ġare Ġconditioned Ġon Ġthe Ġcontext Ġencoding , Ġas Ġdescribed Ġin ĠUID 15 Ġ. ĠTags Ġare Ġgenerated Ġwith Ġan ĠL ST M Ġone Ġcomponent Ġat Ġa Ġtime , Ġe . g . Ġthe Ġtag ĠPRO ; N OM ; SG ; 1 Ġis Ġpredicted Ġas Ġa Ġsequence Ġof Ġfour Ġcomponents , ĠIN LINE FORM 0 ĠPRO , ĠN OM , ĠSG , Ġ1 ĠIN LINE FORM 1 Ġ. </s> For Ġevery Ġtraining Ġinstance , Ġwe Ġback prop agate Ġthe Ġsum Ġof Ġthe Ġmain Ġloss Ġand Ġthe Ġauxiliary Ġloss Ġwithout Ġany Ġweight ing . </s> As ĠMS D Ġtags Ġare Ġonly Ġavailable Ġin ĠTrack Ġ1 , Ġthis Ġaug mentation Ġonly Ġapplies Ġto Ġthis Ġtrack . </s> The Ġparameters Ġof Ġthe Ġentire ĠMS D Ġ( aux iliary - task ) Ġdec oder Ġare Ġshared Ġacross Ġlanguages . </s> Since Ġa Ġgrouping Ġof Ġthe Ġlanguages Ġbased Ġon Ġlanguage Ġfamily Ġwould Ġhave Ġleft Ġseveral Ġlanguages Ġin Ġsingle - member Ġgroups Ġ( e . g . ĠRussian Ġis Ġthe Ġsole Ġrepresentative Ġof Ġthe ĠSlav ic Ġfamily ), Ġwe Ġexperiment Ġwith Ġrandom Ġgroup ings Ġof Ġtwo Ġto Ġthree Ġlanguages . ĠMult ilingual Ġtraining Ġis Ġperformed Ġby Ġrandomly Ġalternating Ġbetween Ġlanguages Ġfor Ġevery Ġnew Ġmin ib atch . ĠWe Ġdo Ġnot Ġpass Ġany Ġinformation Ġto Ġthe Ġauxiliary Ġdec oder Ġas Ġto Ġthe Ġsource Ġlanguage Ġof Ġthe Ġsignal Ġit Ġis Ġreceiving , Ġas Ġwe Ġassume Ġabstract Ġmorph o - sy nt actic Ġfeatures Ġare Ġshared Ġacross Ġlanguages . </s> After Ġ20 Ġepoch s Ġof Ġmult ilingual Ġtraining , Ġwe Ġperform Ġ5 Ġepoch s Ġof Ġmon oling ual Ġfin et uning Ġfor Ġeach Ġlanguage . ĠFor Ġthis Ġphase , Ġwe Ġreduce Ġthe Ġlearning Ġrate Ġto Ġa Ġtenth Ġof Ġthe Ġoriginal Ġlearning Ġrate , Ġi . e . Ġ0 . 0001 , Ġto Ġensure Ġthat Ġthe Ġmodels Ġare Ġindeed Ġbeing Ġfin et uned Ġrather Ġthan Ġret rained . </s> We Ġkeep Ġall Ġhyper param eters Ġthe Ġsame Ġas Ġin Ġthe Ġbaseline . ĠTraining Ġdata Ġis Ġsplit Ġ90 : 10 Ġfor Ġtraining Ġand Ġvalidation . ĠWe Ġtrain Ġour Ġmodels Ġfor Ġ50 Ġepoch s , Ġadding Ġearly Ġstopping Ġwith Ġa Ġtolerance Ġof Ġfive Ġepoch s Ġof Ġno Ġimprovement Ġin Ġthe Ġvalidation Ġloss . ĠWe Ġdo Ġnot Ġsubs ample Ġfrom Ġthe Ġtraining Ġdata . </s> We Ġtrain Ġmodels Ġfor Ġ50 Ġdifferent Ġrandom Ġcombinations Ġof Ġtwo Ġto Ġthree Ġlanguages Ġin ĠTrack Ġ1 , Ġand Ġ50 Ġmon oling ual Ġmodels Ġfor Ġeach Ġlanguage Ġin ĠTrack Ġ2 . ĠInstead Ġof Ġpicking Ġthe Ġsingle Ġmodel Ġthat Ġperforms Ġbest Ġon Ġthe Ġdevelopment Ġset Ġand Ġthus Ġrisking Ġto Ġselect Ġa Ġmodel Ġthat Ġhighly Ġover fits Ġthat Ġdata , Ġwe Ġuse Ġan Ġensemble Ġof Ġthe Ġfive Ġbest Ġmodels , Ġand Ġmake Ġthe Ġfinal Ġprediction Ġfor Ġa Ġgiven Ġtarget Ġform Ġwith Ġa Ġmajority Ġvote Ġover Ġthe Ġfive Ġpredictions . </s> Results Ġand ĠDiscussion </s> Test Ġresults Ġare Ġlisted Ġin ĠTable ĠT AB REF 17 Ġ. ĠOur Ġsystem Ġoutper forms Ġthe Ġbaseline Ġfor Ġall Ġsettings Ġand Ġlanguages Ġin ĠTrack Ġ1 Ġand Ġfor Ġalmost Ġall Ġin ĠTrack Ġ2 âĢĶ only Ġin Ġthe Ġhigh Ġresource Ġsetting Ġis Ġour Ġsystem Ġnot Ġdefinitively Ġsuperior Ġto Ġthe Ġbaseline . </s> Interestingly , Ġour Ġresults Ġin Ġthe Ġlow Ġresource Ġsetting Ġare Ġoften Ġhigher Ġfor ĠTrack Ġ2 Ġthan Ġfor ĠTrack Ġ1 , Ġeven Ġthough Ġcontextual Ġinformation Ġis Ġless Ġexplicit Ġin Ġthe ĠTrack Ġ2 Ġdata Ġand Ġthe Ġmult ilingual Ġmulti - t asking Ġapproach Ġdoes Ġnot Ġapply Ġto Ġthis Ġtrack . ĠWe Ġinterpret Ġthis Ġfinding Ġas Ġan Ġindicator Ġthat Ġa Ġsimpler Ġmodel Ġwith Ġfewer Ġparameters Ġworks Ġbetter Ġin Ġa Ġsetting Ġof Ġlimited Ġtraining Ġdata . ĠNevertheless , Ġwe Ġfocus Ġon Ġthe Ġlow Ġresource Ġsetting Ġin Ġthe Ġanalysis Ġbelow Ġdue Ġto Ġtime Ġlimitations . ĠAs Ġour ĠTrack Ġ1 Ġresults Ġare Ġstill Ġsubstantially Ġhigher Ġthan Ġthe Ġbaseline Ġresults , Ġwe Ġconsider Ġthis Ġanalysis Ġvalid Ġand Ġinsightful . </s> A bl ation ĠStudy </s> We Ġanalyse Ġthe Ġincremental Ġeffect Ġof Ġthe Ġdifferent Ġfeatures Ġin Ġour Ġsystem , Ġfocusing Ġon Ġthe Ġlow - resource Ġsetting Ġin ĠTrack Ġ1 Ġand Ġusing Ġdevelopment Ġdata . </s> Enc oding Ġthe Ġentire Ġcontext Ġwith Ġan ĠL ST M Ġhighly Ġincreases Ġthe Ġvariance Ġof Ġthe Ġobserved Ġresults . ĠSo Ġwe Ġtrained Ġfifty Ġmodels Ġfor Ġeach Ġlanguage Ġand Ġeach Ġarchitecture . ĠFigure ĠFIG REF 23 Ġvisual ises Ġthe Ġmeans Ġand Ġstandard Ġdeviations Ġover Ġthe Ġtrained Ġmodels . ĠIn Ġaddition , Ġwe Ġvisual ise Ġthe Ġaverage Ġaccuracy Ġfor Ġthe Ġfive Ġbest Ġmodels Ġfor Ġeach Ġlanguage Ġand Ġarchitecture , Ġas Ġthese Ġare Ġthe Ġmodels Ġwe Ġuse Ġin Ġthe Ġfinal Ġensemble Ġprediction . ĠBelow Ġwe Ġrefer Ġto Ġthese Ġnumbers Ġonly . </s> The Ġresults Ġindicate Ġthat Ġencoding Ġthe Ġfull Ġcontext Ġwith Ġan ĠL ST M Ġhighly Ġenhances Ġthe Ġperformance Ġof Ġthe Ġmodel , Ġby Ġ11 . 15 % Ġon Ġaverage . ĠThis Ġobservation Ġexplains Ġthe Ġhigh Ġresults Ġwe Ġobtain Ġalso Ġfor ĠTrack Ġ2 . </s> Adding Ġthe Ġauxiliary Ġobjective Ġof ĠMS D Ġprediction Ġhas Ġa Ġvariable Ġeffect : Ġfor Ġfour Ġlanguages Ġ( de , Ġen , Ġes , Ġand Ġsv ) Ġthe Ġeffect Ġis Ġpositive , Ġwhile Ġfor Ġthe Ġrest Ġit Ġis Ġnegative . ĠWe Ġconsider Ġthis Ġto Ġbe Ġan Ġissue Ġof Ġinsufficient Ġdata Ġfor Ġthe Ġtraining Ġof Ġthe Ġauxiliary Ġcomponent Ġin Ġthe Ġlow Ġresource Ġsetting Ġwe Ġare Ġworking Ġwith . </s> We Ġindeed Ġsee Ġresults Ġimproving Ġdrastically Ġwith Ġthe Ġintroduction Ġof Ġmult ilingual Ġtraining , Ġwith Ġmult ilingual Ġresults Ġbeing Ġ7 . 96 % Ġhigher Ġthan Ġmon oling ual Ġones Ġon Ġaverage . </s> We Ġstudied Ġthe Ġfive Ġbest Ġmodels Ġfor Ġeach Ġlanguage Ġas Ġemerging Ġfrom Ġthe Ġmult ilingual Ġtraining Ġ( listed Ġin ĠTable ĠT AB REF 27 Ġ) Ġand Ġfound Ġno Ġstrong Ġlinguistic Ġpatterns . ĠThe Ġen âĢĵ sv Ġpairing Ġseems Ġto Ġyield Ġgood Ġmodels Ġfor Ġthese Ġlanguages , Ġwhich Ġcould Ġbe Ġexplained Ġin Ġterms Ġof Ġtheir Ġcommon Ġlanguage Ġfamily Ġand Ġsimilar Ġmorphology . ĠThe Ġother Ġnatural Ġpair ings , Ġhowever , Ġfr âĢĵ es , Ġand Ġde âĢĵ sv , Ġare Ġnot Ġso Ġfrequent Ġamong Ġthe Ġbest Ġmodels Ġfor Ġthese Ġpairs Ġof Ġlanguages . </s> Finally , Ġmon oling ual Ġfin et uning Ġimproves Ġaccuracy Ġacross Ġthe Ġboard , Ġas Ġone Ġwould Ġexpect , Ġby Ġ2 . 72 % Ġon Ġaverage . </s> The Ġfinal Ġobservation Ġto Ġbe Ġmade Ġbased Ġon Ġthis Ġbreakdown Ġof Ġresults Ġis Ġthat Ġthe Ġmulti - t asking Ġapproach Ġpaired Ġwith Ġmult ilingual Ġtraining Ġand Ġsubsequent Ġmon oling ual Ġfin et uning Ġoutper forms Ġthe Ġother Ġarchitectures Ġfor Ġfive Ġout Ġof Ġseven Ġlanguages : Ġde , Ġen , Ġfr , Ġru Ġand Ġsv . ĠFor Ġthe Ġother Ġtwo Ġlanguages Ġin Ġthe Ġdataset , Ġes Ġand Ġfi , Ġthe Ġdifference Ġbetween Ġthis Ġapproach Ġand Ġthe Ġapproach Ġthat Ġemerged Ġas Ġbest Ġfor Ġthem Ġis Ġless Ġthan Ġ1 %. ĠThe Ġoverall Ġimprovement Ġof Ġthe Ġmult ilingual Ġmulti - t asking Ġapproach Ġover Ġthe Ġbaseline Ġis Ġ18 . 30 %. </s> Error Ġanalysis </s> Here Ġwe Ġstudy Ġthe Ġerrors Ġproduced Ġby Ġour Ġsystem Ġon Ġthe ĠEnglish Ġtest Ġset Ġto Ġbetter Ġunderstand Ġthe Ġremaining Ġshortcomings Ġof Ġthe Ġapproach . ĠA Ġsmall Ġportion Ġof Ġthe Ġwrong Ġpredictions Ġpoint Ġto Ġan Ġincorrect Ġinterpretation Ġof Ġthe Ġmorph o - sy nt actic Ġconditioning Ġof Ġthe Ġcontext , Ġe . g . Ġthe Ġsystem Ġpredicted Ġplan Ġinstead Ġof Ġplans Ġin Ġthe Ġcontext ĠOur Ġ_ Ġinclude Ġraising Ġprivate Ġcapital . ĠThe Ġmajority Ġof Ġwrong Ġpredictions , Ġhowever , Ġare Ġnonsensical , Ġlike Ġbomb Ġfor Ġjob , Ġf ify Ġfor Ġfixing , Ġand Ġg nder rate Ġfor Ġunderstand . ĠThis Ġobservation Ġsuggests Ġthat Ġgenerally Ġthe Ġsystem Ġdid Ġnot Ġlearn Ġto Ġcopy Ġthe Ġcharacters Ġof Ġle mma Ġinto Ġinf lected Ġform , Ġwhich Ġis Ġall Ġit Ġneeds Ġto Ġdo Ġin Ġa Ġlarge Ġnumber Ġof Ġcases . ĠThis Ġissue Ġcould Ġbe Ġallev iated Ġwith Ġsimple Ġdata Ġaug mentation Ġtechniques Ġthat Ġencourage Ġaut oen c oding ĠB IB REF 2 Ġ. </s> MS D Ġprediction </s> Figure ĠFIG REF 32 Ġsummar ises Ġthe Ġaverage ĠMS D - pred iction Ġaccuracy Ġfor Ġthe Ġmulti - t asking Ġexperiments Ġdiscussed Ġabove . ĠAccuracy Ġhere Ġis Ġgenerally Ġhigher Ġthan Ġon Ġthe Ġmain Ġtask , Ġwith Ġthe Ġmult ilingual Ġfin et uned Ġsetup Ġfor ĠSpanish Ġand Ġthe Ġmon oling ual Ġsetup Ġfor ĠFrench Ġscoring Ġbest : Ġ66 . 59 % Ġand Ġ65 . 35 %, Ġrespectively . ĠThis Ġobservation Ġillustrates Ġthe Ġadded Ġdifficulty Ġof Ġgenerating Ġthe Ġcorrect Ġsurface Ġform Ġeven Ġwhen Ġthe Ġmorph os ynt actic Ġdescription Ġhas Ġbeen Ġidentified Ġcorrectly . </s> We Ġobserve Ġsome Ġcorrelation Ġbetween Ġthese Ġnumbers Ġand Ġaccuracy Ġon Ġthe Ġmain Ġtask : Ġfor Ġde , Ġen , Ġru Ġand Ġsv , Ġthe Ġbrown , Ġpink Ġand Ġblue Ġbars Ġhere Ġpattern Ġin Ġthe Ġsame Ġway Ġas Ġthe Ġcorresponding ĠIN LINE FORM 0 Ġ' s Ġin ĠFigure ĠFIG REF 23 Ġ. ĠOne Ġnotable Ġexception Ġto Ġthis Ġpattern Ġis Ġfr Ġwhere Ġinf lection Ġgains Ġa Ġlot Ġfrom Ġmult ilingual Ġtraining , Ġwhile ĠMS D Ġprediction Ġsuffers Ġgreatly . ĠNotice Ġthat Ġthe Ġmagnitude Ġof Ġchange Ġis Ġnot Ġalways Ġthe Ġsame , Ġhowever , Ġeven Ġwhen Ġthe Ġgeneral Ġdirection Ġmatches : Ġfor Ġru , Ġfor Ġexample , Ġmult ilingual Ġtraining Ġbenefits Ġinf lection Ġmuch Ġmore Ġthan Ġin Ġbenefits ĠMS D Ġprediction , Ġeven Ġthough Ġthe ĠMS D Ġdec oder Ġis Ġthe Ġonly Ġcomponent Ġthat Ġis Ġactually Ġshared Ġbetween Ġlanguages . ĠThis Ġobservation Ġillustrates Ġthe Ġtwo - fold Ġeffect Ġof Ġmulti - task Ġtraining : Ġan Ġauxiliary Ġtask Ġcan Ġeither Ġinform Ġthe Ġmain Ġtask Ġthrough Ġthe Ġparameters Ġthe Ġtwo Ġtasks Ġshare , Ġor Ġit Ġcan Ġhelp Ġthe Ġmain Ġtask Ġlearning Ġthrough Ġits Ġregular ising Ġeffect . </s> Related ĠWork </s> Our Ġsystem Ġis Ġinspired Ġby Ġprevious Ġwork Ġon Ġmulti - task Ġlearning Ġand Ġmulti - ling ual Ġlearning , Ġmainly Ġbuilding Ġon Ġtwo Ġintu itions : Ġ( 1 ) Ġjointly Ġlearning Ġrelated Ġtasks Ġtends Ġto Ġbe Ġbeneficial ĠB IB REF 3 Ġ, ĠB IB REF 4 Ġ, ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ; Ġand Ġ( 2 ) Ġjointly Ġlearning Ġrelated Ġlanguages Ġin Ġan ĠM TL - inspired Ġframework Ġtends Ġto Ġbe Ġbeneficial ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ, ĠB IB REF 10 Ġ. ĠIn Ġthe Ġcontext Ġof Ġcomputational Ġmorphology , Ġmulti - ling ual Ġapproaches Ġhave Ġpreviously Ġbeen Ġemployed Ġfor Ġmorph ological Ġrein f lection ĠB IB REF 2 Ġand Ġfor Ġparadigm Ġcompletion ĠB IB REF 11 Ġ. ĠIn Ġboth Ġof Ġthese Ġcases , Ġhowever , Ġthe Ġavailable Ġdatasets Ġcovered Ġmore Ġlanguages , Ġ40 Ġand Ġ21 , Ġrespectively , Ġwhich Ġallowed Ġfor Ġlingu istically - mot ivated Ġlanguage Ġgroup ings Ġand Ġfor Ġparameter Ġsharing Ġdirectly Ġon Ġthe Ġlevel Ġof Ġcharacters . ĠB IB REF 10 Ġexplore Ġparameter Ġsharing Ġbetween Ġrelated Ġlanguages Ġfor Ġdependency Ġparsing , Ġand Ġfind Ġthat Ġsharing Ġis Ġmore Ġbeneficial Ġin Ġthe Ġcase Ġof Ġclosely Ġrelated Ġlanguages . </s> Con clusions </s> In Ġthis Ġpaper Ġwe Ġdescribed Ġour Ġsystem Ġfor Ġthe ĠCo N LL âĢĵ S IG M OR PH ON Ġ2018 Ġshared Ġtask Ġon ĠUniversal ĠMorph ological ĠRe inf lection , ĠTask Ġ2 , Ġwhich Ġachieved Ġthe Ġbest Ġperformance Ġout Ġof Ġall Ġsystems Ġsubmitted , Ġan Ġoverall Ġaccuracy Ġof Ġ49 . 87 . ĠWe Ġshowed Ġin Ġan Ġab lation Ġstudy Ġthat Ġthis Ġis Ġdue Ġto Ġthree Ġcore Ġinnovations , Ġwhich Ġextend Ġa Ġcharacter - based Ġenc oder - dec oder Ġmodel : Ġ( 1 ) Ġa Ġwide Ġcontext Ġwindow , Ġencoding Ġthe Ġentire Ġavailable Ġcontext ; Ġ( 2 ) Ġmulti - task Ġlearning Ġwith Ġthe Ġauxiliary Ġtask Ġof ĠMS D Ġprediction , Ġwhich Ġacts Ġas Ġa Ġregular iser ; Ġ( 3 ) Ġa Ġmult ilingual Ġapproach , Ġexploiting Ġinformation Ġacross Ġlanguages . ĠIn Ġfuture Ġwork Ġwe Ġaim Ġto Ġgain Ġbetter Ġunderstanding Ġof Ġthe Ġincrease Ġin Ġvariance Ġof Ġthe Ġresults Ġintroduced Ġby Ġeach Ġof Ġour Ġmodifications Ġand Ġthe Ġreasons Ġfor Ġthe Ġvarying Ġeffect Ġof Ġmulti - task Ġlearning Ġfor Ġdifferent Ġlanguages . </s> Acknowled gements </s> We Ġgrate fully Ġacknowledge Ġthe Ġsupport Ġof Ġthe ĠNVIDIA ĠCorporation Ġwith Ġthe Ġdonation Ġof Ġthe ĠTitan ĠX p ĠGPU Ġused Ġfor Ġthis Ġresearch .