We also evaluate all five models on downstream tasks from the VecEval suite BIBREF13 , using only the tasks for which training and evaluation data is freely available: chunking, sentiment and question classification, and natural language identification (NLI). The default settings from the suite are used, but we run only the fixed settings, where the embeddings themselves are not tunable parameters of the models, forcing the system to use only the information already in the embeddings.
EXTRACTIVE
We also evaluate all five models on downstream tasks from the VecEval suite BIBREF13 , using only the tasks for which training and evaluation data is freely available: chunking, sentiment and question classification, and natural language identification (NLI). The default settings from the suite are used, but we run only the fixed settings, where the embeddings themselves are not tunable parameters of the models, forcing the system to use only the information already in the embeddings. Finally, we use LV-N, LV-M, and FT to generate OOV word representations for the following words: 1) “hellooo”: a greeting commonly used in instant messaging which emphasizes a syllable. 2) “marvelicious”: a made-up word obtained by merging “marvelous” and “delicious”. 3) “louisana”: a misspelling of the proper name “Louisiana”. 4) “rereread”: recursive use of prefix “re”. 5) “tuzread”: made-up prefix “tuz”.
1805.03710
Introduction </s> Low Ġdimensional Ġword Ġrepresentations Ġ( embed d ings ) Ġhave Ġbecome Ġa Ġkey Ġcomponent Ġin Ġmodern ĠN LP Ġsystems Ġfor Ġlanguage Ġmodeling , Ġparsing , Ġsentiment Ġclassification , Ġand Ġmany Ġothers . ĠThese Ġembed d ings Ġare Ġusually Ġderived Ġby Ġemploying Ġthe Ġdistribution al Ġhypothesis : Ġthat Ġsimilar Ġwords Ġappear Ġin Ġsimilar Ġcontexts ĠB IB REF 0 Ġ. </s> The Ġmodels Ġthat Ġperform Ġthe Ġword Ġembed ding Ġcan Ġbe Ġdivided Ġinto Ġtwo Ġclasses : Ġpredictive , Ġwhich Ġlearn Ġa Ġtarget Ġor Ġcontext Ġword Ġdistribution , Ġand Ġcounting , Ġwhich Ġuse Ġa Ġraw , Ġweighted , Ġor Ġfact ored Ġword - context Ġco - occ urrence Ġmatrix ĠB IB REF 1 Ġ. ĠThe Ġmost Ġwell - known Ġpredictive Ġmodel , Ġwhich Ġhas Ġbecome Ġep onymous Ġwith Ġword Ġembed ding , Ġis Ġword 2 vec ĠB IB REF 2 Ġ. ĠPopular Ġcounting Ġmodels Ġinclude ĠP PM I - S VD ĠB IB REF 3 Ġ, ĠGl o Ve ĠB IB REF 4 Ġ, Ġand ĠLex V ec ĠB IB REF 5 Ġ. </s> These Ġmodels Ġall Ġlearn Ġword - level Ġrepresentations , Ġwhich Ġpresents Ġtwo Ġmain Ġproblems : Ġ1 ) ĠLearned Ġinformation Ġis Ġnot Ġexplicitly Ġshared Ġamong Ġthe Ġrepresentations Ġas Ġeach Ġword Ġhas Ġan Ġindependent Ġvector . Ġ2 ) ĠThere Ġis Ġno Ġclear Ġway Ġto Ġrepresent Ġout - of - voc abulary Ġ( OO V ) Ġwords . </s> fast Text ĠB IB REF 6 Ġaddresses Ġthese Ġissues Ġin Ġthe ĠSkip - gram Ġword 2 vec Ġmodel Ġby Ġrepresenting Ġa Ġword Ġby Ġthe Ġsum Ġof Ġa Ġunique Ġvector Ġand Ġa Ġset Ġof Ġshared Ġcharacter Ġn - gram s Ġ( from Ġhere on Ġsimply Ġreferred Ġto Ġas Ġn - gram s ) Ġvectors . ĠThis Ġaddresses Ġboth Ġissues Ġabove Ġas Ġlearned Ġinformation Ġis Ġshared Ġthrough Ġthe Ġn - gram Ġvectors Ġand Ġfrom Ġthese ĠO OV Ġword Ġrepresentations Ġcan Ġbe Ġconstructed . </s> In Ġthis Ġpaper Ġwe Ġpropose Ġincorporating Ġsub word Ġinformation Ġinto Ġcounting Ġmodels Ġusing Ġa Ġstrategy Ġsimilar Ġto Ġfast Text . </s> We Ġuse ĠLex V ec Ġas Ġthe Ġcounting Ġmodel Ġas Ġit Ġgenerally Ġoutper forms ĠP PM I - S VD Ġand ĠGl o Ve Ġon Ġintrinsic Ġand Ġextr ins ic Ġevaluations ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ, ĠB IB REF 10 Ġ, Ġbut Ġthe Ġmethod Ġproposed Ġhere Ġshould Ġtransfer Ġto ĠGl o Ve Ġunchanged . </s> The ĠLex V ec Ġobjective Ġis Ġmodified Ġsuch Ġthat Ġa Ġword 's Ġvector Ġis Ġthe Ġsum Ġof Ġall Ġits Ġsub word Ġvectors . </s> We Ġcompare Ġ1 ) Ġthe Ġuse Ġof Ġn - gram Ġsub words , Ġlike Ġfast Text , Ġand Ġ2 ) Ġun super vised Ġmorp hem es Ġidentified Ġusing ĠMor f essor ĠB IB REF 11 Ġto Ġlearn Ġwhether Ġmore Ġlingu istically Ġmotivated Ġsub words Ġoffer Ġany Ġadvantage Ġover Ġsimple Ġn - gram s . </s> To Ġevaluate Ġthe Ġimpact Ġsub word Ġinformation Ġhas Ġon Ġin - voc abulary Ġ( IV ) Ġword Ġrepresentations , Ġwe Ġrun Ġintrinsic Ġevaluations Ġconsisting Ġof Ġword Ġsimilarity Ġand Ġword Ġanalogy Ġtasks . ĠThe Ġincorporation Ġof Ġsub word Ġinformation Ġresults Ġin Ġsimilar Ġgains Ġ( and Ġlosses ) Ġto Ġthat Ġof Ġfast Text Ġover ĠSkip - gram . ĠWhereas Ġincorporating Ġn - gram Ġsub words Ġtends Ġto Ġcapture Ġmore Ġsynt actic Ġinformation , Ġun super vised Ġmorp hem es Ġbetter Ġpreserve Ġsemantics Ġwhile Ġalso Ġimproving Ġsynt actic Ġresults . ĠGiven Ġthat Ġintrinsic Ġperformance Ġcan Ġcorrelate Ġpoorly Ġwith Ġperformance Ġon Ġdownstream Ġtasks ĠB IB REF 12 Ġ, Ġwe Ġalso Ġconduct Ġevaluation Ġusing Ġthe ĠVec E val Ġsuite Ġof Ġtasks ĠB IB REF 13 Ġ, Ġin Ġwhich </s> all Ġsub word Ġmodels , Ġincluding Ġfast Text , Ġshow Ġno Ġsignificant Ġimprovement Ġover Ġword - level Ġmodels . </s> We Ġverify Ġthe Ġmodel 's Ġability Ġto Ġrepresent ĠO OV Ġwords Ġby Ġquant itatively Ġevaluating Ġnearest - ne igh bors . ĠResults Ġshow Ġthat , Ġlike Ġfast Text , Ġboth ĠLex V ec Ġn - gram Ġand Ġ( to Ġa Ġlesser Ġdegree ) Ġun super vised Ġmor phe me Ġmodels Ġgive Ġcoherent Ġanswers . </s> This Ġpaper Ġdiscusses Ġrelated Ġword Ġ( Ġ$ Â§ $ Ġ" Related ĠWork " Ġ), Ġintroduces Ġthe Ġsub word ĠLex V ec Ġmodel Ġ( Ġ$ Â§ $ Ġ" Sub word ĠLex V ec " Ġ), Ġdescribes Ġexperiments Ġ( Ġ$ Â§ $ Ġ" Materials " Ġ), Ġanaly zes Ġresults Ġ( Ġ$ Â§ $ Ġ" Results " Ġ), Ġand Ġconcludes Ġwith Ġideas Ġfor Ġfuture Ġworks Ġ( Ġ$ Â§ $ Ġ" Conclusion Ġand ĠFuture ĠWork " Ġ). </s> Related ĠWork </s> Word Ġembed d ings Ġthat Ġleverage Ġsub word Ġinformation Ġwere Ġfirst Ġintroduced Ġby ĠB IB REF 14 Ġwhich Ġrepresented Ġa Ġword Ġof Ġas Ġthe Ġsum Ġof Ġfour - gram Ġvectors Ġobtained Ġrunning Ġan ĠS VD Ġof Ġa Ġfour - gram Ġto Ġfour - gram Ġco - occ urrence Ġmatrix . ĠOur Ġmodel Ġdiffers Ġby Ġlearning Ġthe Ġsub word Ġvectors Ġand Ġresulting Ġrepresentation Ġjointly Ġas Ġweighted Ġfactor ization Ġof Ġa Ġword - context Ġco - occ urrence Ġmatrix Ġis Ġperformed . </s> There Ġare Ġmany Ġmodels Ġthat Ġuse Ġcharacter - level Ġsub word Ġinformation Ġto Ġform Ġword Ġrepresentations ĠB IB REF 15 Ġ, ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ, ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, Ġas Ġwell Ġas Ġfast Text Ġ( the Ġmodel Ġon Ġwhich Ġwe Ġbase Ġour Ġwork ). ĠClose ly Ġrelated Ġare Ġmodels Ġthat Ġuse Ġmorph ological Ġsegment ation Ġin Ġlearning Ġword Ġrepresentations ĠB IB REF 20 Ġ, ĠB IB REF 21 Ġ, ĠB IB REF 22 Ġ, ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġ, ĠB IB REF 25 Ġ. ĠOur Ġmodel Ġalso Ġuses Ġn - gram s Ġand Ġmorph ological Ġsegment ation , Ġbut Ġit Ġperforms Ġexplicit Ġmatrix Ġfactor ization Ġto Ġlearn Ġsub word Ġand Ġword Ġrepresentations , Ġunlike Ġthese Ġrelated Ġmodels Ġwhich Ġmostly Ġuse Ġneural Ġnetworks . </s> Finally , ĠB IB REF 26 Ġand ĠB IB REF 27 Ġretro fit Ġmorph ological Ġinformation Ġonto Ġpre - trained Ġmodels . ĠThese Ġdiffer Ġfrom Ġour Ġwork Ġin Ġthat Ġwe Ġincorporate Ġmorph ological Ġinformation Ġat Ġtraining Ġtime , Ġand Ġthat Ġonly ĠB IB REF 26 Ġis Ġable Ġto Ġgenerate Ġembed d ings Ġfor ĠO OV Ġwords . </s> Sub word ĠLex V ec </s> The ĠLex V ec ĠB IB REF 7 Ġmodel Ġfactor izes Ġthe ĠP PM I - weight ed Ġword - context Ġco - occ urrence Ġmatrix Ġusing Ġsto ch astic Ġgradient Ġdescent . </s> $$ P PM I _{ w c } Ġ= Ġmax ( 0 , Ġ\ log Ġ\ frac { M _{ w c } Ġ\ ; ĠM _{ ** }} { ĠM _{ w * } Ġ\ ; ĠM _{ * c } Ġ}) $$ Ġ Ġ Ġ( E q . Ġ3 ) </s> where Ġ$ M $ Ġis Ġthe Ġword - context Ġco - occ urrence Ġmatrix Ġconstructed Ġby Ġsliding Ġa Ġwindow Ġof Ġfixed Ġsize Ġcentered Ġover Ġevery Ġtarget Ġword </s> $ w $ Ġin Ġthe Ġsubs am pled ĠB IB REF 2 Ġtraining Ġcorpus Ġand Ġincrement ing Ġcell Ġ$ M _{ w c } $ Ġfor Ġevery Ġcontext Ġword Ġ$ c $ Ġappearing Ġwithin Ġthis Ġwindow Ġ( forming Ġa Ġ$( w , c ) $ Ġpair ). ĠLex V ec Ġadjusts Ġthe ĠP PM I Ġmatrix Ġusing Ġcontext Ġdistribution Ġsmoot hing ĠB IB REF 3 Ġ. </s> With Ġthe ĠP PM I Ġmatrix Ġcalculated , Ġthe Ġsliding Ġwindow Ġprocess Ġis Ġrepeated Ġand Ġthe Ġfollowing Ġloss Ġfunctions Ġare Ġminimized Ġfor Ġevery Ġobserved Ġ$( w , c ) $ Ġpair Ġand Ġtarget Ġword Ġ$ w $ Ġ: </s> $$ L _{ w c } Ġ& = Ġ\ frac { 1 }{ 2 } Ġ( u _ w ^ \ top Ġv _ c Ġ- ĠP PM I _{ w c }) ^ 2 Ġ\\ Ġ ĠL _{ w } Ġ& = Ġ\ frac { 1 }{ 2 } Ġ\ sum Ġ\ limits Ġ_ { i = 1 } ^ k {\ math bf Ġ{ E } _{ c _ i Ġ\ sim ĠP _ n ( c )} Ġ( u _ w ^ \ top Ġv _{ c _ i } Ġ- ĠP PM I _{ w c _ i }) ^ 2 Ġ} $$ Ġ Ġ Ġ( E q . Ġ4 ) </s> where Ġ$ u _ w $ Ġand Ġ$ v _ c $ Ġare Ġ$ d $ Ġ- dimensional Ġword Ġand Ġcontext Ġvectors . ĠThe Ġsecond Ġloss Ġfunction Ġdescribes Ġhow , Ġfor Ġeach Ġtarget Ġword , Ġ$ k $ Ġnegative Ġsamples ĠB IB REF 2 Ġare Ġdrawn Ġfrom Ġthe Ġsmoot hed Ġcontext Ġun ig ram Ġdistribution . </s> Given Ġa Ġset Ġof Ġsub words Ġ$ S _ w $ Ġfor Ġa Ġword Ġ$ w $ Ġ, Ġwe Ġfollow Ġfast Text Ġand Ġreplace Ġ$ u _ w $ Ġin Ġeq : lex vec 2 , eq : lex vec 3 Ġby Ġ$ u ^ {\ prime Ġ} _ w $ Ġsuch Ġthat : </s> $$ u ^ {\ prime Ġ} _ w Ġ= Ġ\ frac { 1 }{ | S _ w | Ġ+ Ġ1 } Ġ( u _ w Ġ+ Ġ\ sum Ġ_ { s Ġ\ in ĠS _ w } Ġq _{ hash ( s ) }) $$ Ġ Ġ Ġ( E q . Ġ5 ) </s> such Ġthat Ġa Ġword Ġis Ġthe Ġsum Ġof Ġits Ġword Ġvector Ġand Ġits Ġ$ d $ Ġ- dimensional Ġsub word Ġvectors Ġ$ q _ x $ Ġ. ĠThe Ġnumber Ġof Ġpossible Ġsub words Ġis Ġvery Ġlarge Ġso Ġthe Ġfunction Ġ$ hash ( s ) $ Ġhashes Ġa Ġsub word Ġto Ġthe Ġinterval Ġ$ [ 1 , Ġbuckets ] $ Ġ. ĠFor ĠO OV Ġwords , </s> $$ u ^ {\ prime Ġ} _ w Ġ= Ġ\ frac { 1 }{ | S _ w | } Ġ\ sum Ġ_ { s Ġ\ in ĠS _ w } Ġq _{ hash ( s )} $$ Ġ Ġ Ġ( E q . Ġ7 ) </s> We Ġcompare Ġtwo Ġtypes Ġof Ġsub words : Ġsimple Ġn - gram s Ġ( like Ġfast Text ) Ġand Ġun super vised Ġmorp hem es . ĠFor Ġexample , Ġgiven Ġthe Ġword ĠâĢ ľ cat âĢ Ŀ , Ġwe Ġmark Ġbeginning Ġand Ġend Ġwith Ġangled Ġbrackets Ġand Ġuse Ġall Ġn - gram s Ġof Ġlength Ġ3 Ġto Ġ6 Ġas Ġsub words , Ġyielding Ġ$ S _{ \ text normal Ġ{ cat }} Ġ= Ġ\ l brace Ġ Ġ\ text normal Ġ{ $ Ġ$ Ġca , Ġat $ Ġ$ , Ġcat } Ġ\ r brace Ġ$ Ġ. ĠMor f essor ĠB IB REF 11 Ġis Ġused Ġto Ġprob abil istically Ġsegment Ġwords Ġinto Ġmorp hem es . ĠThe ĠMor f essor Ġmodel Ġis Ġtrained Ġusing Ġraw Ġtext Ġso Ġit Ġis Ġentirely Ġun super vised . ĠFor Ġthe Ġword ĠâĢ ľ sub sequent âĢ Ŀ , Ġwe Ġget Ġ$ S _{ \ text normal Ġ{ sub sequent }} Ġ= Ġ\ l brace Ġ Ġ\ text normal Ġ{ $ Ġ$ Ġsub , Ġsequ ent $ Ġ$ } Ġ\ r brace Ġ$ Ġ. </s> Materials </s> Our Ġexperiments Ġaim Ġto Ġmeasure Ġif Ġthe Ġincorporation Ġof Ġsub word Ġinformation Ġinto ĠLex V ec Ġresults Ġin Ġsimilar Ġimprovements Ġas Ġobserved Ġin Ġmoving Ġfrom ĠSkip - gram Ġto Ġfast Text , Ġand Ġwhether Ġun super vised Ġmorp hem es Ġoffer Ġany Ġadvantage Ġover Ġn - gram s . ĠFor ĠIV Ġwords , Ġwe Ġperform Ġintrinsic Ġevaluation Ġvia Ġword Ġsimilarity Ġand Ġword Ġanalogy Ġtasks , Ġas Ġwell Ġas Ġdownstream Ġtasks . ĠO OV Ġword Ġrepresentation Ġis Ġtested Ġthrough Ġqualitative Ġnearest - ne igh bor Ġanalysis . </s> All Ġmodels Ġare Ġtrained Ġusing Ġa Ġ2015 Ġdump Ġof ĠWikipedia , Ġlower c ased Ġand Ġusing Ġonly Ġal phan umeric Ġcharacters . ĠVoc abulary Ġis Ġlimited Ġto Ġwords Ġthat Ġappear Ġat Ġleast Ġ100 Ġtimes Ġfor Ġa Ġtotal Ġof Ġ30 35 17 Ġwords . ĠMor f essor Ġis Ġtrained Ġon Ġthis Ġvocabulary Ġlist . </s> We Ġtrain Ġthe Ġstandard ĠLex V ec Ġ( LV ), ĠLex V ec Ġusing Ġn - gram s Ġ( LV - N ), Ġand ĠLex V ec Ġusing Ġun super vised Ġmorp hem es Ġ( LV - M ) Ġusing Ġthe Ġsame Ġhyper - param eters Ġas ĠB IB REF 7 Ġ( Ġ$\ text normal Ġ{ window } Ġ= Ġ2 $ Ġ, Ġ$\ text normal Ġ{ initial Ġlearning Ġrate } Ġ= Ġ. 025 $ Ġ, Ġ$\ text normal Ġ{ sub sam pling } Ġ= Ġ10 ^{ - 5 } $ Ġ, Ġ$\ text normal Ġ{ negative Ġsamples } Ġ= Ġ5 $ Ġ, Ġ$\ text normal Ġ{ context Ġdistribution Ġsmoot hing } Ġ= Ġ. 75 $ Ġ, Ġ$\ text normal Ġ{ pos itional Ġcontexts } Ġ= Ġ\ text normal Ġ{ True } $ Ġ). </s> Both ĠSkip - gram Ġ( SG ) Ġand Ġfast Text Ġ( FT ) Ġare Ġtrained Ġusing Ġthe Ġreference Ġimplementation Ġof Ġfast Text Ġwith Ġthe Ġhyper - param eters Ġgiven Ġby ĠB IB REF 6 Ġ( Ġ$\ text normal Ġ{ window } Ġ= Ġ5 $ Ġ, Ġ$\ text normal Ġ{ initial Ġlearning Ġrate } Ġ= Ġ. 025 $ Ġ, Ġ$\ text normal Ġ{ sub sam pling } Ġ= Ġ10 ^{ - 4 } $ Ġ, Ġ$\ text normal Ġ{ negative Ġsamples } Ġ= Ġ5 $ Ġ). </s> All Ġfive Ġmodels Ġare Ġrun Ġfor Ġ5 Ġiterations Ġover Ġthe Ġtraining Ġcorpus Ġand Ġgenerate Ġ300 Ġdimensional Ġword Ġrepresentations . ĠLV - N , ĠLV - M , Ġand ĠFT Ġuse Ġ200 0000 Ġbuckets Ġwhen Ġhashing Ġsub words . </s> For Ġword Ġsimilarity Ġevaluations , Ġwe Ġuse Ġthe ĠWord Sim - 353 ĠSimilar ity Ġ( WS - Sim ) Ġand ĠRelated ness Ġ( WS - Rel ) ĠB IB REF 28 Ġand ĠSim Lex - 999 Ġ( Sim Lex ) ĠB IB REF 29 Ġdatasets , Ġand Ġthe ĠRare ĠWord Ġ( RW ) ĠB IB REF 20 Ġdataset Ġto Ġverify Ġif Ġsub word Ġinformation Ġimproves Ġrare Ġword Ġrepresentation . ĠRelations hips Ġare Ġmeasured Ġusing Ġthe ĠGoogle Ġsemantic Ġ( G Sem ) Ġand Ġsynt actic Ġ( GS yn ) Ġanalog ies ĠB IB REF 2 Ġand Ġthe ĠMicrosoft Ġsynt actic Ġanalog ies Ġ( MS R ) Ġdataset ĠB IB REF 30 Ġ. </s> We Ġalso Ġevaluate Ġall Ġfive Ġmodels Ġon Ġdownstream Ġtasks Ġfrom Ġthe ĠVec E val Ġsuite ĠB IB REF 13 Ġ, Ġusing Ġonly Ġthe Ġtasks Ġfor Ġwhich Ġtraining Ġand Ġevaluation Ġdata Ġis Ġfreely Ġavailable : Ġchunk ing , Ġsentiment Ġand Ġquestion Ġclassification , Ġand Ġnatural Ġlanguage Ġidentification Ġ( N LI ). ĠThe Ġdefault Ġsettings Ġfrom Ġthe Ġsuite Ġare Ġused , Ġbut Ġwe Ġrun Ġonly Ġthe Ġfixed Ġsettings , Ġwhere Ġthe Ġembed d ings Ġthemselves Ġare Ġnot Ġtun able Ġparameters Ġof Ġthe Ġmodels , Ġforcing Ġthe Ġsystem Ġto Ġuse Ġonly Ġthe Ġinformation Ġalready Ġin Ġthe Ġembed d ings . </s> Finally , Ġwe Ġuse ĠLV - N , ĠLV - M , Ġand ĠFT Ġto Ġgenerate ĠO OV Ġword Ġrepresentations Ġfor Ġthe Ġfollowing Ġwords : Ġ1 ) ĠâĢ ľ hell ooo âĢ Ŀ : Ġa Ġgreeting Ġcommonly Ġused Ġin Ġinstant Ġmessaging Ġwhich Ġemphasizes Ġa Ġsyll able . Ġ2 ) ĠâĢ ľ mar vel icious âĢ Ŀ : Ġa Ġmade - up Ġword Ġobtained Ġby Ġmerging ĠâĢ ľ mar vel ous âĢ Ŀ Ġand ĠâĢ ľ del icious âĢ Ŀ . Ġ3 ) ĠâĢ ľ l ou is ana âĢ Ŀ : Ġa Ġmiss pelling Ġof Ġthe Ġproper Ġname ĠâĢ ľ Louis iana âĢ Ŀ . Ġ4 ) ĠâĢ ľ re re read âĢ Ŀ : Ġrecursive Ġuse Ġof Ġprefix ĠâĢ ľ re âĢ Ŀ . Ġ5 ) ĠâĢ ľ t uz read âĢ Ŀ : Ġmade - up Ġprefix ĠâĢ ľ t uz âĢ Ŀ . </s> Results </s> Results Ġfor ĠIV Ġevaluation Ġare Ġshown Ġin Ġtab : int r ins ic , Ġand Ġfor ĠO OV Ġin Ġtab : o ov . </s> Like Ġin ĠFT , Ġthe Ġuse Ġof Ġsub word Ġinformation Ġin Ġboth ĠLV - N Ġand ĠLV - M Ġresults Ġin Ġ1 ) Ġbetter Ġrepresentation Ġof Ġrare Ġwords , Ġas Ġevidenced Ġby Ġthe Ġincrease Ġin ĠRW Ġcorrelation , Ġand Ġ2 ) Ġsignificant Ġimprovement Ġon Ġthe ĠGS yn Ġand ĠMS R Ġtasks , Ġin Ġevidence Ġof Ġsub words Ġencoding Ġinformation Ġabout Ġa Ġword 's Ġsynt actic Ġfunction Ġ( the Ġsuffix ĠâĢ ľ ly âĢ Ŀ , Ġfor Ġexample , Ġsuggests Ġan Ġad verb ). </s> There Ġseems Ġto Ġa Ġtrade - off Ġbetween Ġcapturing Ġsemantics Ġand Ġsyntax Ġas Ġin Ġboth ĠLV - N Ġand ĠFT Ġthere Ġis Ġan Ġaccompanying Ġdecrease Ġon Ġthe ĠG Sem Ġtasks Ġin Ġexchange Ġfor Ġgains Ġon Ġthe ĠGS yn Ġand ĠMS R Ġtasks . ĠMorph ological Ġsegment ation Ġin ĠLV - M Ġappears Ġto Ġfavor Ġsyntax Ġless Ġstrongly Ġthan Ġdo Ġsimple Ġn - gram s . </s> On Ġthe Ġdownstream Ġtasks , Ġwe Ġonly Ġobserve Ġstatistically Ġsignificant Ġ( Ġ$ p Ġ< Ġ. 05 $ Ġunder Ġa Ġrandom Ġperm utation Ġtest ) Ġimprovement Ġon Ġthe Ġchunk ing Ġtask , Ġand Ġit Ġis Ġa Ġvery Ġsmall Ġgain . ĠWe Ġattribute Ġthis Ġto Ġboth Ġregular Ġand Ġsub word Ġmodels Ġhaving Ġvery Ġsimilar Ġquality Ġon Ġfrequent ĠIV Ġword Ġrepresentation . ĠStat istically , Ġthese Ġare Ġthe Ġwords Ġare Ġthat Ġare Ġmost Ġlikely Ġto Ġappear Ġin Ġthe Ġdownstream Ġtask Ġinstances , Ġand Ġso Ġthe Ġsuperior Ġrepresentation Ġof Ġrare Ġwords </s> has , Ġdue Ġto Ġtheir Ġnature , Ġlittle Ġimpact Ġon Ġoverall Ġaccuracy . ĠBecause Ġin Ġall Ġtasks ĠO OV Ġwords Ġare Ġmapped Ġto Ġthe ĠâĢ ľ Ġ$\ l angle Ġ$ Ġun k Ġ$\ r angle Ġ$ ĠâĢ Ŀ Ġtoken , Ġthe Ġsub word Ġmodels Ġare Ġnot Ġbeing Ġused Ġto Ġthe Ġfullest , Ġand Ġin Ġfuture Ġwork Ġwe Ġwill Ġinvestigate Ġwhether Ġgenerating Ġrepresentations Ġfor Ġall Ġwords Ġimproves Ġtask Ġperformance . </s> In ĠO OV Ġrepresentation Ġ( tab : o ov ), ĠLV - N Ġand ĠFT Ġwork Ġalmost Ġident ically , Ġas Ġis Ġto Ġbe Ġexpected . ĠBoth Ġfind Ġhighly Ġcoherent Ġneighbors Ġfor Ġthe Ġwords ĠâĢ ľ hell ooo âĢ Ŀ , ĠâĢ ľ mar vel icious âĢ Ŀ , Ġand ĠâĢ ľ re re read âĢ Ŀ . ĠInterestingly , Ġthe Ġmiss pelling Ġof ĠâĢ ľ l ou is ana âĢ Ŀ Ġleads Ġto Ġcoherent Ġname - like Ġneighbors , Ġalthough Ġnone Ġis Ġthe Ġexpected Ġcorrect Ġspelling ĠâĢ ľ l ou is iana âĢ Ŀ . ĠAll Ġmodels Ġstumble Ġon Ġthe Ġmade - up Ġprefix ĠâĢ ľ t uz âĢ Ŀ . ĠA Ġpossible Ġfix Ġwould Ġbe Ġto Ġdown - we igh Ġvery Ġrare Ġsub words Ġin Ġthe Ġvector Ġsumm ation . ĠLV - M Ġis Ġless Ġrobust Ġthan ĠLV - N Ġand ĠFT Ġon Ġthis Ġtask Ġas Ġit Ġis Ġhighly Ġsensitive Ġto Ġincorrect Ġsegment ation , Ġexempl ified Ġin Ġthe ĠâĢ ľ hell ooo âĢ Ŀ Ġexample . </s> Finally , Ġwe Ġsee Ġthat Ġnearest - ne igh bors Ġare Ġa Ġmixture Ġof Ġsimilarly Ġpre / suff ixed Ġwords . ĠIf Ġthese Ġpre / suff ix es Ġare Ġsemantic , Ġthe Ġneighbors Ġare Ġsem antically Ġrelated , Ġelse Ġif Ġsynt actic Ġthey Ġhave Ġsimilar Ġsynt actic Ġfunction . ĠThis Ġsuggests Ġthat Ġit Ġshould Ġbe Ġpossible Ġto Ġget Ġtun able Ġrepresentations Ġwhich Ġare Ġmore Ġdriven Ġby Ġsemantics Ġor Ġsyntax Ġby Ġa Ġweighted Ġsumm ation Ġof Ġsub word Ġvectors , Ġgiven Ġwe Ġcan Ġidentify Ġwhether Ġa Ġpre / suff ix Ġis Ġsemantic Ġor Ġsynt actic Ġin Ġnature Ġand Ġweigh Ġthem Ġaccordingly . ĠThis Ġmight Ġbe Ġpossible Ġwithout Ġsupervision Ġusing Ġcorpus Ġstatistics Ġas Ġsynt actic Ġsub words Ġare Ġlikely Ġto Ġbe Ġmore Ġfrequent , Ġand Ġso Ġcould Ġbe Ġdown - weight ed Ġfor Ġmore Ġsemantic Ġrepresentations . ĠThis Ġis Ġsomething Ġwe Ġwill Ġpursue Ġin Ġfuture Ġwork . </s> Conclusion Ġand ĠFuture ĠWork </s> In Ġthis Ġpaper , Ġwe Ġincorporated Ġsub word Ġinformation Ġ( simple Ġn - gram s Ġand Ġun super vised Ġmorp hem es ) Ġinto Ġthe ĠLex V ec Ġword Ġembed ding Ġmodel Ġand Ġevaluated Ġits Ġimpact Ġon Ġthe Ġresulting ĠIV Ġand ĠO OV Ġword Ġvectors . ĠLike Ġfast Text , Ġsub word ĠLex V ec Ġlearns Ġbetter Ġrepresentations Ġfor Ġrare Ġwords Ġthan Ġits Ġword - level Ġcounterpart . ĠAll Ġmodels Ġgenerated Ġcoherent Ġrepresentations Ġfor ĠO OV Ġwords , Ġwith Ġsimple Ġn - gram s Ġdemonstrating Ġmore Ġrobust ness Ġthan Ġun super vised Ġmorp hem es . ĠIn Ġfuture Ġwork , Ġwe Ġwill Ġverify Ġwhether Ġusing ĠO OV Ġrepresentations Ġin Ġdownstream Ġtasks Ġimproves Ġperformance . ĠWe Ġwill Ġalso Ġexplore Ġthe Ġtrade - off Ġbetween Ġsemantics Ġand Ġsyntax Ġwhen Ġsub word Ġinformation Ġis Ġused .
Introduction Low dimensional word representations (embeddings) have become a key component in modern NLP systems for language modeling, parsing, sentiment classification, and many others. These embeddings are usually derived by employing the distributional hypothesis: that similar words appear in similar contexts BIBREF0 . The models that perform the word embedding can be divided into two classes: predictive, which learn a target or context word distribution, and counting, which use a raw, weighted, or factored word-context co-occurrence matrix BIBREF1 . The most well-known predictive model, which has become eponymous with word embedding, is word2vec BIBREF2 . Popular counting models include PPMI-SVD BIBREF3 , GloVe BIBREF4 , and LexVec BIBREF5 . These models all learn word-level representations, which presents two main problems: 1) Learned information is not explicitly shared among the representations as each word has an independent vector. 2) There is no clear way to represent out-of-vocabulary (OOV) words. fastText BIBREF6 addresses these issues in the Skip-gram word2vec model by representing a word by the sum of a unique vector and a set of shared character n-grams (from hereon simply referred to as n-grams) vectors. This addresses both issues above as learned information is shared through the n-gram vectors and from these OOV word representations can be constructed. In this paper we propose incorporating subword information into counting models using a strategy similar to fastText. We use LexVec as the counting model as it generally outperforms PPMI-SVD and GloVe on intrinsic and extrinsic evaluations BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , but the method proposed here should transfer to GloVe unchanged. The LexVec objective is modified such that a word's vector is the sum of all its subword vectors. We compare 1) the use of n-gram subwords, like fastText, and 2) unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords offer any advantage over simple n-grams. To evaluate the impact subword information has on in-vocabulary (IV) word representations, we run intrinsic evaluations consisting of word similarity and word analogy tasks. The incorporation of subword information results in similar gains (and losses) to that of fastText over Skip-gram. Whereas incorporating n-gram subwords tends to capture more syntactic information, unsupervised morphemes better preserve semantics while also improving syntactic results. Given that intrinsic performance can correlate poorly with performance on downstream tasks BIBREF12 , we also conduct evaluation using the VecEval suite of tasks BIBREF13 , in which all subword models, including fastText, show no significant improvement over word-level models. We verify the model's ability to represent OOV words by quantitatively evaluating nearest-neighbors. Results show that, like fastText, both LexVec n-gram and (to a lesser degree) unsupervised morpheme models give coherent answers. This paper discusses related word ( $§$ "Related Work" ), introduces the subword LexVec model ( $§$ "Subword LexVec" ), describes experiments ( $§$ "Materials" ), analyzes results ( $§$ "Results" ), and concludes with ideas for future works ( $§$ "Conclusion and Future Work" ). Related Work Word embeddings that leverage subword information were first introduced by BIBREF14 which represented a word of as the sum of four-gram vectors obtained running an SVD of a four-gram to four-gram co-occurrence matrix. Our model differs by learning the subword vectors and resulting representation jointly as weighted factorization of a word-context co-occurrence matrix is performed. There are many models that use character-level subword information to form word representations BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , as well as fastText (the model on which we base our work). Closely related are models that use morphological segmentation in learning word representations BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 . Our model also uses n-grams and morphological segmentation, but it performs explicit matrix factorization to learn subword and word representations, unlike these related models which mostly use neural networks. Finally, BIBREF26 and BIBREF27 retrofit morphological information onto pre-trained models. These differ from our work in that we incorporate morphological information at training time, and that only BIBREF26 is able to generate embeddings for OOV words. Subword LexVec The LexVec BIBREF7 model factorizes the PPMI-weighted word-context co-occurrence matrix using stochastic gradient descent. $$PPMI_{wc} = max(0, \log \frac{M_{wc} \; M_{**}}{ M_{w*} \; M_{*c} })$$   (Eq. 3) where $M$ is the word-context co-occurrence matrix constructed by sliding a window of fixed size centered over every target word $w$ in the subsampled BIBREF2 training corpus and incrementing cell $M_{wc}$ for every context word $c$ appearing within this window (forming a $(w,c)$ pair). LexVec adjusts the PPMI matrix using context distribution smoothing BIBREF3 . With the PPMI matrix calculated, the sliding window process is repeated and the following loss functions are minimized for every observed $(w,c)$ pair and target word $w$ : $$L_{wc} &= \frac{1}{2} (u_w^\top v_c - PPMI_{wc})^2 \\  L_{w} &= \frac{1}{2} \sum \limits _{i=1}^k{\mathbf {E}_{c_i \sim P_n(c)} (u_w^\top v_{c_i} - PPMI_{wc_i})^2 }$$   (Eq. 4) where $u_w$ and $v_c$ are $d$ -dimensional word and context vectors. The second loss function describes how, for each target word, $k$ negative samples BIBREF2 are drawn from the smoothed context unigram distribution. Given a set of subwords $S_w$ for a word $w$ , we follow fastText and replace $u_w$ in eq:lexvec2,eq:lexvec3 by $u^{\prime }_w$ such that: $$u^{\prime }_w = \frac{1}{|S_w| + 1} (u_w + \sum _{s \in S_w} q_{hash(s)})$$   (Eq. 5) such that a word is the sum of its word vector and its $d$ -dimensional subword vectors $q_x$ . The number of possible subwords is very large so the function $hash(s)$ hashes a subword to the interval $[1, buckets]$ . For OOV words, $$u^{\prime }_w = \frac{1}{|S_w|} \sum _{s \in S_w} q_{hash(s)}$$   (Eq. 7) We compare two types of subwords: simple n-grams (like fastText) and unsupervised morphemes. For example, given the word “cat”, we mark beginning and end with angled brackets and use all n-grams of length 3 to 6 as subwords, yielding $S_{\textnormal {cat}} = \lbrace  \textnormal {$ $ ca, at$ $, cat} \rbrace $ . Morfessor BIBREF11 is used to probabilistically segment words into morphemes. The Morfessor model is trained using raw text so it is entirely unsupervised. For the word “subsequent”, we get $S_{\textnormal {subsequent}} = \lbrace  \textnormal {$ $ sub, sequent$ $} \rbrace $ . Materials Our experiments aim to measure if the incorporation of subword information into LexVec results in similar improvements as observed in moving from Skip-gram to fastText, and whether unsupervised morphemes offer any advantage over n-grams. For IV words, we perform intrinsic evaluation via word similarity and word analogy tasks, as well as downstream tasks. OOV word representation is tested through qualitative nearest-neighbor analysis. All models are trained using a 2015 dump of Wikipedia, lowercased and using only alphanumeric characters. Vocabulary is limited to words that appear at least 100 times for a total of 303517 words. Morfessor is trained on this vocabulary list. We train the standard LexVec (LV), LexVec using n-grams (LV-N), and LexVec using unsupervised morphemes (LV-M) using the same hyper-parameters as BIBREF7 ( $\textnormal {window} = 2$ , $\textnormal {initial learning rate} = .025$ , $\textnormal {subsampling} = 10^{-5}$ , $\textnormal {negative samples} = 5$ , $\textnormal {context distribution smoothing} = .75$ , $\textnormal {positional contexts} = \textnormal {True}$ ). Both Skip-gram (SG) and fastText (FT) are trained using the reference implementation of fastText with the hyper-parameters given by BIBREF6 ( $\textnormal {window} = 5$ , $\textnormal {initial learning rate} = .025$ , $\textnormal {subsampling} = 10^{-4}$ , $\textnormal {negative samples} = 5$ ). All five models are run for 5 iterations over the training corpus and generate 300 dimensional word representations. LV-N, LV-M, and FT use 2000000 buckets when hashing subwords. For word similarity evaluations, we use the WordSim-353 Similarity (WS-Sim) and Relatedness (WS-Rel) BIBREF28 and SimLex-999 (SimLex) BIBREF29 datasets, and the Rare Word (RW) BIBREF20 dataset to verify if subword information improves rare word representation. Relationships are measured using the Google semantic (GSem) and syntactic (GSyn) analogies BIBREF2 and the Microsoft syntactic analogies (MSR) dataset BIBREF30 . We also evaluate all five models on downstream tasks from the VecEval suite BIBREF13 , using only the tasks for which training and evaluation data is freely available: chunking, sentiment and question classification, and natural language identification (NLI). The default settings from the suite are used, but we run only the fixed settings, where the embeddings themselves are not tunable parameters of the models, forcing the system to use only the information already in the embeddings. Finally, we use LV-N, LV-M, and FT to generate OOV word representations for the following words: 1) “hellooo”: a greeting commonly used in instant messaging which emphasizes a syllable. 2) “marvelicious”: a made-up word obtained by merging “marvelous” and “delicious”. 3) “louisana”: a misspelling of the proper name “Louisiana”. 4) “rereread”: recursive use of prefix “re”. 5) “tuzread”: made-up prefix “tuz”. Results Results for IV evaluation are shown in tab:intrinsic, and for OOV in tab:oov. Like in FT, the use of subword information in both LV-N and LV-M results in 1) better representation of rare words, as evidenced by the increase in RW correlation, and 2) significant improvement on the GSyn and MSR tasks, in evidence of subwords encoding information about a word's syntactic function (the suffix “ly”, for example, suggests an adverb). There seems to a trade-off between capturing semantics and syntax as in both LV-N and FT there is an accompanying decrease on the GSem tasks in exchange for gains on the GSyn and MSR tasks. Morphological segmentation in LV-M appears to favor syntax less strongly than do simple n-grams. On the downstream tasks, we only observe statistically significant ( $p < .05$ under a random permutation test) improvement on the chunking task, and it is a very small gain. We attribute this to both regular and subword models having very similar quality on frequent IV word representation. Statistically, these are the words are that are most likely to appear in the downstream task instances, and so the superior representation of rare words has, due to their nature, little impact on overall accuracy. Because in all tasks OOV words are mapped to the “ $\langle $ unk $\rangle $ ” token, the subword models are not being used to the fullest, and in future work we will investigate whether generating representations for all words improves task performance. In OOV representation (tab:oov), LV-N and FT work almost identically, as is to be expected. Both find highly coherent neighbors for the words “hellooo”, “marvelicious”, and “rereread”. Interestingly, the misspelling of “louisana” leads to coherent name-like neighbors, although none is the expected correct spelling “louisiana”. All models stumble on the made-up prefix “tuz”. A possible fix would be to down-weigh very rare subwords in the vector summation. LV-M is less robust than LV-N and FT on this task as it is highly sensitive to incorrect segmentation, exemplified in the “hellooo” example. Finally, we see that nearest-neighbors are a mixture of similarly pre/suffixed words. If these pre/suffixes are semantic, the neighbors are semantically related, else if syntactic they have similar syntactic function. This suggests that it should be possible to get tunable representations which are more driven by semantics or syntax by a weighted summation of subword vectors, given we can identify whether a pre/suffix is semantic or syntactic in nature and weigh them accordingly. This might be possible without supervision using corpus statistics as syntactic subwords are likely to be more frequent, and so could be down-weighted for more semantic representations. This is something we will pursue in future work. Conclusion and Future Work In this paper, we incorporated subword information (simple n-grams and unsupervised morphemes) into the LexVec word embedding model and evaluated its impact on the resulting IV and OOV word vectors. Like fastText, subword LexVec learns better representations for rare words than its word-level counterpart. All models generated coherent representations for OOV words, with simple n-grams demonstrating more robustness than unsupervised morphemes. In future work, we will verify whether using OOV representations in downstream tasks improves performance. We will also explore the trade-off between semantics and syntax when subword information is used.
How do they evaluate their resulting word embeddings?
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How Ġdo Ġthey Ġevaluate Ġtheir Ġresulting Ġword Ġembed d ings ?
We Ġalso Ġevaluate Ġall Ġfive Ġmodels Ġon Ġdownstream Ġtasks Ġfrom Ġthe ĠVec E val Ġsuite ĠB IB REF 13 Ġ, Ġusing Ġonly Ġthe Ġtasks Ġfor Ġwhich Ġtraining Ġand Ġevaluation Ġdata Ġis Ġfreely Ġavailable : Ġchunk ing , Ġsentiment Ġand Ġquestion Ġclassification , Ġand Ġnatural Ġlanguage Ġidentification Ġ( N LI ). ĠThe Ġdefault Ġsettings Ġfrom Ġthe Ġsuite Ġare Ġused , Ġbut Ġwe Ġrun Ġonly Ġthe Ġfixed Ġsettings , Ġwhere Ġthe Ġembed d ings Ġthemselves Ġare Ġnot Ġtun able Ġparameters Ġof Ġthe Ġmodels , Ġforcing Ġthe Ġsystem Ġto Ġuse Ġonly Ġthe Ġinformation Ġalready Ġin Ġthe Ġembed d ings .
<s> How Ġdo Ġthey Ġevaluate Ġtheir Ġresulting Ġword Ġembed d ings ? </s> Introduction </s> Low Ġdimensional Ġword Ġrepresentations Ġ( embed d ings ) Ġhave Ġbecome Ġa Ġkey Ġcomponent Ġin Ġmodern ĠN LP Ġsystems Ġfor Ġlanguage Ġmodeling , Ġparsing , Ġsentiment Ġclassification , Ġand Ġmany Ġothers . ĠThese Ġembed d ings Ġare Ġusually Ġderived Ġby Ġemploying Ġthe Ġdistribution al Ġhypothesis : Ġthat Ġsimilar Ġwords Ġappear Ġin Ġsimilar Ġcontexts ĠB IB REF 0 Ġ. </s> The Ġmodels Ġthat Ġperform Ġthe Ġword Ġembed ding Ġcan Ġbe Ġdivided Ġinto Ġtwo Ġclasses : Ġpredictive , Ġwhich Ġlearn Ġa Ġtarget Ġor Ġcontext Ġword Ġdistribution , Ġand Ġcounting , Ġwhich Ġuse Ġa Ġraw , Ġweighted , Ġor Ġfact ored Ġword - context Ġco - occ urrence Ġmatrix ĠB IB REF 1 Ġ. ĠThe Ġmost Ġwell - known Ġpredictive Ġmodel , Ġwhich Ġhas Ġbecome Ġep onymous Ġwith Ġword Ġembed ding , Ġis Ġword 2 vec ĠB IB REF 2 Ġ. ĠPopular Ġcounting Ġmodels Ġinclude ĠP PM I - S VD ĠB IB REF 3 Ġ, ĠGl o Ve ĠB IB REF 4 Ġ, Ġand ĠLex V ec ĠB IB REF 5 Ġ. </s> These Ġmodels Ġall Ġlearn Ġword - level Ġrepresentations , Ġwhich Ġpresents Ġtwo Ġmain Ġproblems : Ġ1 ) ĠLearned Ġinformation Ġis Ġnot Ġexplicitly Ġshared Ġamong Ġthe Ġrepresentations Ġas Ġeach Ġword Ġhas Ġan Ġindependent Ġvector . Ġ2 ) ĠThere Ġis Ġno Ġclear Ġway Ġto Ġrepresent Ġout - of - voc abulary Ġ( OO V ) Ġwords . </s> fast Text ĠB IB REF 6 Ġaddresses Ġthese Ġissues Ġin Ġthe ĠSkip - gram Ġword 2 vec Ġmodel Ġby Ġrepresenting Ġa Ġword Ġby Ġthe Ġsum Ġof Ġa Ġunique Ġvector Ġand Ġa Ġset Ġof Ġshared Ġcharacter Ġn - gram s Ġ( from Ġhere on Ġsimply Ġreferred Ġto Ġas Ġn - gram s ) Ġvectors . ĠThis Ġaddresses Ġboth Ġissues Ġabove Ġas Ġlearned Ġinformation Ġis Ġshared Ġthrough Ġthe Ġn - gram Ġvectors Ġand Ġfrom Ġthese ĠO OV Ġword Ġrepresentations Ġcan Ġbe Ġconstructed . </s> In Ġthis Ġpaper Ġwe Ġpropose Ġincorporating Ġsub word Ġinformation Ġinto Ġcounting Ġmodels Ġusing Ġa Ġstrategy Ġsimilar Ġto Ġfast Text . </s> We Ġuse ĠLex V ec Ġas Ġthe Ġcounting Ġmodel Ġas Ġit Ġgenerally Ġoutper forms ĠP PM I - S VD Ġand ĠGl o Ve Ġon Ġintrinsic Ġand Ġextr ins ic Ġevaluations ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ, ĠB IB REF 10 Ġ, Ġbut Ġthe Ġmethod Ġproposed Ġhere Ġshould Ġtransfer Ġto ĠGl o Ve Ġunchanged . </s> The ĠLex V ec Ġobjective Ġis Ġmodified Ġsuch Ġthat Ġa Ġword 's Ġvector Ġis Ġthe Ġsum Ġof Ġall Ġits Ġsub word Ġvectors . </s> We Ġcompare Ġ1 ) Ġthe Ġuse Ġof Ġn - gram Ġsub words , Ġlike Ġfast Text , Ġand Ġ2 ) Ġun super vised Ġmorp hem es Ġidentified Ġusing ĠMor f essor ĠB IB REF 11 Ġto Ġlearn Ġwhether Ġmore Ġlingu istically Ġmotivated Ġsub words Ġoffer Ġany Ġadvantage Ġover Ġsimple Ġn - gram s . </s> To Ġevaluate Ġthe Ġimpact Ġsub word Ġinformation Ġhas Ġon Ġin - voc abulary Ġ( IV ) Ġword Ġrepresentations , Ġwe Ġrun Ġintrinsic Ġevaluations Ġconsisting Ġof Ġword Ġsimilarity Ġand Ġword Ġanalogy Ġtasks . ĠThe Ġincorporation Ġof Ġsub word Ġinformation Ġresults Ġin Ġsimilar Ġgains Ġ( and Ġlosses ) Ġto Ġthat Ġof Ġfast Text Ġover ĠSkip - gram . ĠWhereas Ġincorporating Ġn - gram Ġsub words Ġtends Ġto Ġcapture Ġmore Ġsynt actic Ġinformation , Ġun super vised Ġmorp hem es Ġbetter Ġpreserve Ġsemantics Ġwhile Ġalso Ġimproving Ġsynt actic Ġresults . ĠGiven Ġthat Ġintrinsic Ġperformance Ġcan Ġcorrelate Ġpoorly Ġwith Ġperformance Ġon Ġdownstream Ġtasks ĠB IB REF 12 Ġ, Ġwe Ġalso Ġconduct Ġevaluation Ġusing Ġthe ĠVec E val Ġsuite Ġof Ġtasks ĠB IB REF 13 Ġ, Ġin Ġwhich </s> all Ġsub word Ġmodels , Ġincluding Ġfast Text , Ġshow Ġno Ġsignificant Ġimprovement Ġover Ġword - level Ġmodels . </s> We Ġverify Ġthe Ġmodel 's Ġability Ġto Ġrepresent ĠO OV Ġwords Ġby Ġquant itatively Ġevaluating Ġnearest - ne igh bors . ĠResults Ġshow Ġthat , Ġlike Ġfast Text , Ġboth ĠLex V ec Ġn - gram Ġand Ġ( to Ġa Ġlesser Ġdegree ) Ġun super vised Ġmor phe me Ġmodels Ġgive Ġcoherent Ġanswers . </s> This Ġpaper Ġdiscusses Ġrelated Ġword Ġ( Ġ$ Â§ $ Ġ" Related ĠWork " Ġ), Ġintroduces Ġthe Ġsub word ĠLex V ec Ġmodel Ġ( Ġ$ Â§ $ Ġ" Sub word ĠLex V ec " Ġ), Ġdescribes Ġexperiments Ġ( Ġ$ Â§ $ Ġ" Materials " Ġ), Ġanaly zes Ġresults Ġ( Ġ$ Â§ $ Ġ" Results " Ġ), Ġand Ġconcludes Ġwith Ġideas Ġfor Ġfuture Ġworks Ġ( Ġ$ Â§ $ Ġ" Conclusion Ġand ĠFuture ĠWork " Ġ). </s> Related ĠWork </s> Word Ġembed d ings Ġthat Ġleverage Ġsub word Ġinformation Ġwere Ġfirst Ġintroduced Ġby ĠB IB REF 14 Ġwhich Ġrepresented Ġa Ġword Ġof Ġas Ġthe Ġsum Ġof Ġfour - gram Ġvectors Ġobtained Ġrunning Ġan ĠS VD Ġof Ġa Ġfour - gram Ġto Ġfour - gram Ġco - occ urrence Ġmatrix . ĠOur Ġmodel Ġdiffers Ġby Ġlearning Ġthe Ġsub word Ġvectors Ġand Ġresulting Ġrepresentation Ġjointly Ġas Ġweighted Ġfactor ization Ġof Ġa Ġword - context Ġco - occ urrence Ġmatrix Ġis Ġperformed . </s> There Ġare Ġmany Ġmodels Ġthat Ġuse Ġcharacter - level Ġsub word Ġinformation Ġto Ġform Ġword Ġrepresentations ĠB IB REF 15 Ġ, ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ, ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ, Ġas Ġwell Ġas Ġfast Text Ġ( the Ġmodel Ġon Ġwhich Ġwe Ġbase Ġour Ġwork ). ĠClose ly Ġrelated Ġare Ġmodels Ġthat Ġuse Ġmorph ological Ġsegment ation Ġin Ġlearning Ġword Ġrepresentations ĠB IB REF 20 Ġ, ĠB IB REF 21 Ġ, ĠB IB REF 22 Ġ, ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġ, ĠB IB REF 25 Ġ. ĠOur Ġmodel Ġalso Ġuses Ġn - gram s Ġand Ġmorph ological Ġsegment ation , Ġbut Ġit Ġperforms Ġexplicit Ġmatrix Ġfactor ization Ġto Ġlearn Ġsub word Ġand Ġword Ġrepresentations , Ġunlike Ġthese Ġrelated Ġmodels Ġwhich Ġmostly Ġuse Ġneural Ġnetworks . </s> Finally , ĠB IB REF 26 Ġand ĠB IB REF 27 Ġretro fit Ġmorph ological Ġinformation Ġonto Ġpre - trained Ġmodels . ĠThese Ġdiffer Ġfrom Ġour Ġwork Ġin Ġthat Ġwe Ġincorporate Ġmorph ological Ġinformation Ġat Ġtraining Ġtime , Ġand Ġthat Ġonly ĠB IB REF 26 Ġis Ġable Ġto Ġgenerate Ġembed d ings Ġfor ĠO OV Ġwords . </s> Sub word ĠLex V ec </s> The ĠLex V ec ĠB IB REF 7 Ġmodel Ġfactor izes Ġthe ĠP PM I - weight ed Ġword - context Ġco - occ urrence Ġmatrix Ġusing Ġsto ch astic Ġgradient Ġdescent . </s> $$ P PM I _{ w c } Ġ= Ġmax ( 0 , Ġ\ log Ġ\ frac { M _{ w c } Ġ\ ; ĠM _{ ** }} { ĠM _{ w * } Ġ\ ; ĠM _{ * c } Ġ}) $$ Ġ Ġ Ġ( E q . Ġ3 ) </s> where Ġ$ M $ Ġis Ġthe Ġword - context Ġco - occ urrence Ġmatrix Ġconstructed Ġby Ġsliding Ġa Ġwindow Ġof Ġfixed Ġsize Ġcentered Ġover Ġevery Ġtarget Ġword </s> $ w $ Ġin Ġthe Ġsubs am pled ĠB IB REF 2 Ġtraining Ġcorpus Ġand Ġincrement ing Ġcell Ġ$ M _{ w c } $ Ġfor Ġevery Ġcontext Ġword Ġ$ c $ Ġappearing Ġwithin Ġthis Ġwindow Ġ( forming Ġa Ġ$( w , c ) $ Ġpair ). ĠLex V ec Ġadjusts Ġthe ĠP PM I Ġmatrix Ġusing Ġcontext Ġdistribution Ġsmoot hing ĠB IB REF 3 Ġ. </s> With Ġthe ĠP PM I Ġmatrix Ġcalculated , Ġthe Ġsliding Ġwindow Ġprocess Ġis Ġrepeated Ġand Ġthe Ġfollowing Ġloss Ġfunctions Ġare Ġminimized Ġfor Ġevery Ġobserved Ġ$( w , c ) $ Ġpair Ġand Ġtarget Ġword Ġ$ w $ Ġ: </s> $$ L _{ w c } Ġ& = Ġ\ frac { 1 }{ 2 } Ġ( u _ w ^ \ top Ġv _ c Ġ- ĠP PM I _{ w c }) ^ 2 Ġ\\ Ġ ĠL _{ w } Ġ& = Ġ\ frac { 1 }{ 2 } Ġ\ sum Ġ\ limits Ġ_ { i = 1 } ^ k {\ math bf Ġ{ E } _{ c _ i Ġ\ sim ĠP _ n ( c )} Ġ( u _ w ^ \ top Ġv _{ c _ i } Ġ- ĠP PM I _{ w c _ i }) ^ 2 Ġ} $$ Ġ Ġ Ġ( E q . Ġ4 ) </s> where Ġ$ u _ w $ Ġand Ġ$ v _ c $ Ġare Ġ$ d $ Ġ- dimensional Ġword Ġand Ġcontext Ġvectors . ĠThe Ġsecond Ġloss Ġfunction Ġdescribes Ġhow , Ġfor Ġeach Ġtarget Ġword , Ġ$ k $ Ġnegative Ġsamples ĠB IB REF 2 Ġare Ġdrawn Ġfrom Ġthe Ġsmoot hed Ġcontext Ġun ig ram Ġdistribution . </s> Given Ġa Ġset Ġof Ġsub words Ġ$ S _ w $ Ġfor Ġa Ġword Ġ$ w $ Ġ, Ġwe Ġfollow Ġfast Text Ġand Ġreplace Ġ$ u _ w $ Ġin Ġeq : lex vec 2 , eq : lex vec 3 Ġby Ġ$ u ^ {\ prime Ġ} _ w $ Ġsuch Ġthat : </s> $$ u ^ {\ prime Ġ} _ w Ġ= Ġ\ frac { 1 }{ | S _ w | Ġ+ Ġ1 } Ġ( u _ w Ġ+ Ġ\ sum Ġ_ { s Ġ\ in ĠS _ w } Ġq _{ hash ( s ) }) $$ Ġ Ġ Ġ( E q . Ġ5 ) </s> such Ġthat Ġa Ġword Ġis Ġthe Ġsum Ġof Ġits Ġword Ġvector Ġand Ġits Ġ$ d $ Ġ- dimensional Ġsub word Ġvectors Ġ$ q _ x $ Ġ. ĠThe Ġnumber Ġof Ġpossible Ġsub words Ġis Ġvery Ġlarge Ġso Ġthe Ġfunction Ġ$ hash ( s ) $ Ġhashes Ġa Ġsub word Ġto Ġthe Ġinterval Ġ$ [ 1 , Ġbuckets ] $ Ġ. ĠFor ĠO OV Ġwords , </s> $$ u ^ {\ prime Ġ} _ w Ġ= Ġ\ frac { 1 }{ | S _ w | } Ġ\ sum Ġ_ { s Ġ\ in ĠS _ w } Ġq _{ hash ( s )} $$ Ġ Ġ Ġ( E q . Ġ7 ) </s> We Ġcompare Ġtwo Ġtypes Ġof Ġsub words : Ġsimple Ġn - gram s Ġ( like Ġfast Text ) Ġand Ġun super vised Ġmorp hem es . ĠFor Ġexample , Ġgiven Ġthe Ġword ĠâĢ ľ cat âĢ Ŀ , Ġwe Ġmark Ġbeginning Ġand Ġend Ġwith Ġangled Ġbrackets Ġand Ġuse Ġall Ġn - gram s Ġof Ġlength Ġ3 Ġto Ġ6 Ġas Ġsub words , Ġyielding Ġ$ S _{ \ text normal Ġ{ cat }} Ġ= Ġ\ l brace Ġ Ġ\ text normal Ġ{ $ Ġ$ Ġca , Ġat $ Ġ$ , Ġcat } Ġ\ r brace Ġ$ Ġ. ĠMor f essor ĠB IB REF 11 Ġis Ġused Ġto Ġprob abil istically Ġsegment Ġwords Ġinto Ġmorp hem es . ĠThe ĠMor f essor Ġmodel Ġis Ġtrained Ġusing Ġraw Ġtext Ġso Ġit Ġis Ġentirely Ġun super vised . ĠFor Ġthe Ġword ĠâĢ ľ sub sequent âĢ Ŀ , Ġwe Ġget Ġ$ S _{ \ text normal Ġ{ sub sequent }} Ġ= Ġ\ l brace Ġ Ġ\ text normal Ġ{ $ Ġ$ Ġsub , Ġsequ ent $ Ġ$ } Ġ\ r brace Ġ$ Ġ. </s> Materials </s> Our Ġexperiments Ġaim Ġto Ġmeasure Ġif Ġthe Ġincorporation Ġof Ġsub word Ġinformation Ġinto ĠLex V ec Ġresults Ġin Ġsimilar Ġimprovements Ġas Ġobserved Ġin Ġmoving Ġfrom ĠSkip - gram Ġto Ġfast Text , Ġand Ġwhether Ġun super vised Ġmorp hem es Ġoffer Ġany Ġadvantage Ġover Ġn - gram s . ĠFor ĠIV Ġwords , Ġwe Ġperform Ġintrinsic Ġevaluation Ġvia Ġword Ġsimilarity Ġand Ġword Ġanalogy Ġtasks , Ġas Ġwell Ġas Ġdownstream Ġtasks . ĠO OV Ġword Ġrepresentation Ġis Ġtested Ġthrough Ġqualitative Ġnearest - ne igh bor Ġanalysis . </s> All Ġmodels Ġare Ġtrained Ġusing Ġa Ġ2015 Ġdump Ġof ĠWikipedia , Ġlower c ased Ġand Ġusing Ġonly Ġal phan umeric Ġcharacters . ĠVoc abulary Ġis Ġlimited Ġto Ġwords Ġthat Ġappear Ġat Ġleast Ġ100 Ġtimes Ġfor Ġa Ġtotal Ġof Ġ30 35 17 Ġwords . ĠMor f essor Ġis Ġtrained Ġon Ġthis Ġvocabulary Ġlist . </s> We Ġtrain Ġthe Ġstandard ĠLex V ec Ġ( LV ), ĠLex V ec Ġusing Ġn - gram s Ġ( LV - N ), Ġand ĠLex V ec Ġusing Ġun super vised Ġmorp hem es Ġ( LV - M ) Ġusing Ġthe Ġsame Ġhyper - param eters Ġas ĠB IB REF 7 Ġ( Ġ$\ text normal Ġ{ window } Ġ= Ġ2 $ Ġ, Ġ$\ text normal Ġ{ initial Ġlearning Ġrate } Ġ= Ġ. 025 $ Ġ, Ġ$\ text normal Ġ{ sub sam pling } Ġ= Ġ10 ^{ - 5 } $ Ġ, Ġ$\ text normal Ġ{ negative Ġsamples } Ġ= Ġ5 $ Ġ, Ġ$\ text normal Ġ{ context Ġdistribution Ġsmoot hing } Ġ= Ġ. 75 $ Ġ, Ġ$\ text normal Ġ{ pos itional Ġcontexts } Ġ= Ġ\ text normal Ġ{ True } $ Ġ). </s> Both ĠSkip - gram Ġ( SG ) Ġand Ġfast Text Ġ( FT ) Ġare Ġtrained Ġusing Ġthe Ġreference Ġimplementation Ġof Ġfast Text Ġwith Ġthe Ġhyper - param eters Ġgiven Ġby ĠB IB REF 6 Ġ( Ġ$\ text normal Ġ{ window } Ġ= Ġ5 $ Ġ, Ġ$\ text normal Ġ{ initial Ġlearning Ġrate } Ġ= Ġ. 025 $ Ġ, Ġ$\ text normal Ġ{ sub sam pling } Ġ= Ġ10 ^{ - 4 } $ Ġ, Ġ$\ text normal Ġ{ negative Ġsamples } Ġ= Ġ5 $ Ġ). </s> All Ġfive Ġmodels Ġare Ġrun Ġfor Ġ5 Ġiterations Ġover Ġthe Ġtraining Ġcorpus Ġand Ġgenerate Ġ300 Ġdimensional Ġword Ġrepresentations . ĠLV - N , ĠLV - M , Ġand ĠFT Ġuse Ġ200 0000 Ġbuckets Ġwhen Ġhashing Ġsub words . </s> For Ġword Ġsimilarity Ġevaluations , Ġwe Ġuse Ġthe ĠWord Sim - 353 ĠSimilar ity Ġ( WS - Sim ) Ġand ĠRelated ness Ġ( WS - Rel ) ĠB IB REF 28 Ġand ĠSim Lex - 999 Ġ( Sim Lex ) ĠB IB REF 29 Ġdatasets , Ġand Ġthe ĠRare ĠWord Ġ( RW ) ĠB IB REF 20 Ġdataset Ġto Ġverify Ġif Ġsub word Ġinformation Ġimproves Ġrare Ġword Ġrepresentation . ĠRelations hips Ġare Ġmeasured Ġusing Ġthe ĠGoogle Ġsemantic Ġ( G Sem ) Ġand Ġsynt actic Ġ( GS yn ) Ġanalog ies ĠB IB REF 2 Ġand Ġthe ĠMicrosoft Ġsynt actic Ġanalog ies Ġ( MS R ) Ġdataset ĠB IB REF 30 Ġ. </s> We Ġalso Ġevaluate Ġall Ġfive Ġmodels Ġon Ġdownstream Ġtasks Ġfrom Ġthe ĠVec E val Ġsuite ĠB IB REF 13 Ġ, Ġusing Ġonly Ġthe Ġtasks Ġfor Ġwhich Ġtraining Ġand Ġevaluation Ġdata Ġis Ġfreely Ġavailable : Ġchunk ing , Ġsentiment Ġand Ġquestion Ġclassification , Ġand Ġnatural Ġlanguage Ġidentification Ġ( N LI ). ĠThe Ġdefault Ġsettings Ġfrom Ġthe Ġsuite Ġare Ġused , Ġbut Ġwe Ġrun Ġonly Ġthe Ġfixed Ġsettings , Ġwhere Ġthe Ġembed d ings Ġthemselves Ġare Ġnot Ġtun able Ġparameters Ġof Ġthe Ġmodels , Ġforcing Ġthe Ġsystem Ġto Ġuse Ġonly Ġthe Ġinformation Ġalready Ġin Ġthe Ġembed d ings . </s> Finally , Ġwe Ġuse ĠLV - N , ĠLV - M , Ġand ĠFT Ġto Ġgenerate ĠO OV Ġword Ġrepresentations Ġfor Ġthe Ġfollowing Ġwords : Ġ1 ) ĠâĢ ľ hell ooo âĢ Ŀ : Ġa Ġgreeting Ġcommonly Ġused Ġin Ġinstant Ġmessaging Ġwhich Ġemphasizes Ġa Ġsyll able . Ġ2 ) ĠâĢ ľ mar vel icious âĢ Ŀ : Ġa Ġmade - up Ġword Ġobtained Ġby Ġmerging ĠâĢ ľ mar vel ous âĢ Ŀ Ġand ĠâĢ ľ del icious âĢ Ŀ . Ġ3 ) ĠâĢ ľ l ou is ana âĢ Ŀ : Ġa Ġmiss pelling Ġof Ġthe Ġproper Ġname ĠâĢ ľ Louis iana âĢ Ŀ . Ġ4 ) ĠâĢ ľ re re read âĢ Ŀ : Ġrecursive Ġuse Ġof Ġprefix ĠâĢ ľ re âĢ Ŀ . Ġ5 ) ĠâĢ ľ t uz read âĢ Ŀ : Ġmade - up Ġprefix ĠâĢ ľ t uz âĢ Ŀ . </s> Results </s> Results Ġfor ĠIV Ġevaluation Ġare Ġshown Ġin Ġtab : int r ins ic , Ġand Ġfor ĠO OV Ġin Ġtab : o ov . </s> Like Ġin ĠFT , Ġthe Ġuse Ġof Ġsub word Ġinformation Ġin Ġboth ĠLV - N Ġand ĠLV - M Ġresults Ġin Ġ1 ) Ġbetter Ġrepresentation Ġof Ġrare Ġwords , Ġas Ġevidenced Ġby Ġthe Ġincrease Ġin ĠRW Ġcorrelation , Ġand Ġ2 ) Ġsignificant Ġimprovement Ġon Ġthe ĠGS yn Ġand ĠMS R Ġtasks , Ġin Ġevidence Ġof Ġsub words Ġencoding Ġinformation Ġabout Ġa Ġword 's Ġsynt actic Ġfunction Ġ( the Ġsuffix ĠâĢ ľ ly âĢ Ŀ , Ġfor Ġexample , Ġsuggests Ġan Ġad verb ). </s> There Ġseems Ġto Ġa Ġtrade - off Ġbetween Ġcapturing Ġsemantics Ġand Ġsyntax Ġas Ġin Ġboth ĠLV - N Ġand ĠFT Ġthere Ġis Ġan Ġaccompanying Ġdecrease Ġon Ġthe ĠG Sem Ġtasks Ġin Ġexchange Ġfor Ġgains Ġon Ġthe ĠGS yn Ġand ĠMS R Ġtasks . ĠMorph ological Ġsegment ation Ġin ĠLV - M Ġappears Ġto Ġfavor Ġsyntax Ġless Ġstrongly Ġthan Ġdo Ġsimple Ġn - gram s . </s> On Ġthe Ġdownstream Ġtasks , Ġwe Ġonly Ġobserve Ġstatistically Ġsignificant Ġ( Ġ$ p Ġ< Ġ. 05 $ Ġunder Ġa Ġrandom Ġperm utation Ġtest ) Ġimprovement Ġon Ġthe Ġchunk ing Ġtask , Ġand Ġit Ġis Ġa Ġvery Ġsmall Ġgain . ĠWe Ġattribute Ġthis Ġto Ġboth Ġregular Ġand Ġsub word Ġmodels Ġhaving Ġvery Ġsimilar Ġquality Ġon Ġfrequent ĠIV Ġword Ġrepresentation . ĠStat istically , Ġthese Ġare Ġthe Ġwords Ġare Ġthat Ġare Ġmost Ġlikely Ġto Ġappear Ġin Ġthe Ġdownstream Ġtask Ġinstances , Ġand Ġso Ġthe Ġsuperior Ġrepresentation Ġof Ġrare Ġwords </s> has , Ġdue Ġto Ġtheir Ġnature , Ġlittle Ġimpact Ġon Ġoverall Ġaccuracy . ĠBecause Ġin Ġall Ġtasks ĠO OV Ġwords Ġare Ġmapped Ġto Ġthe ĠâĢ ľ Ġ$\ l angle Ġ$ Ġun k Ġ$\ r angle Ġ$ ĠâĢ Ŀ Ġtoken , Ġthe Ġsub word Ġmodels Ġare Ġnot Ġbeing Ġused Ġto Ġthe Ġfullest , Ġand Ġin Ġfuture Ġwork Ġwe Ġwill Ġinvestigate Ġwhether Ġgenerating Ġrepresentations Ġfor Ġall Ġwords Ġimproves Ġtask Ġperformance . </s> In ĠO OV Ġrepresentation Ġ( tab : o ov ), ĠLV - N Ġand ĠFT Ġwork Ġalmost Ġident ically , Ġas Ġis Ġto Ġbe Ġexpected . ĠBoth Ġfind Ġhighly Ġcoherent Ġneighbors Ġfor Ġthe Ġwords ĠâĢ ľ hell ooo âĢ Ŀ , ĠâĢ ľ mar vel icious âĢ Ŀ , Ġand ĠâĢ ľ re re read âĢ Ŀ . ĠInterestingly , Ġthe Ġmiss pelling Ġof ĠâĢ ľ l ou is ana âĢ Ŀ Ġleads Ġto Ġcoherent Ġname - like Ġneighbors , Ġalthough Ġnone Ġis Ġthe Ġexpected Ġcorrect Ġspelling ĠâĢ ľ l ou is iana âĢ Ŀ . ĠAll Ġmodels Ġstumble Ġon Ġthe Ġmade - up Ġprefix ĠâĢ ľ t uz âĢ Ŀ . ĠA Ġpossible Ġfix Ġwould Ġbe Ġto Ġdown - we igh Ġvery Ġrare Ġsub words Ġin Ġthe Ġvector Ġsumm ation . ĠLV - M Ġis Ġless Ġrobust Ġthan ĠLV - N Ġand ĠFT Ġon Ġthis Ġtask Ġas Ġit Ġis Ġhighly Ġsensitive Ġto Ġincorrect Ġsegment ation , Ġexempl ified Ġin Ġthe ĠâĢ ľ hell ooo âĢ Ŀ Ġexample . </s> Finally , Ġwe Ġsee Ġthat Ġnearest - ne igh bors Ġare Ġa Ġmixture Ġof Ġsimilarly Ġpre / suff ixed Ġwords . ĠIf Ġthese Ġpre / suff ix es Ġare Ġsemantic , Ġthe Ġneighbors Ġare Ġsem antically Ġrelated , Ġelse Ġif Ġsynt actic Ġthey Ġhave Ġsimilar Ġsynt actic Ġfunction . ĠThis Ġsuggests Ġthat Ġit Ġshould Ġbe Ġpossible Ġto Ġget Ġtun able Ġrepresentations Ġwhich Ġare Ġmore Ġdriven Ġby Ġsemantics Ġor Ġsyntax Ġby Ġa Ġweighted Ġsumm ation Ġof Ġsub word Ġvectors , Ġgiven Ġwe Ġcan Ġidentify Ġwhether Ġa Ġpre / suff ix Ġis Ġsemantic Ġor Ġsynt actic Ġin Ġnature Ġand Ġweigh Ġthem Ġaccordingly . ĠThis Ġmight Ġbe Ġpossible Ġwithout Ġsupervision Ġusing Ġcorpus Ġstatistics Ġas Ġsynt actic Ġsub words Ġare Ġlikely Ġto Ġbe Ġmore Ġfrequent , Ġand Ġso Ġcould Ġbe Ġdown - weight ed Ġfor Ġmore Ġsemantic Ġrepresentations . ĠThis Ġis Ġsomething Ġwe Ġwill Ġpursue Ġin Ġfuture Ġwork . </s> Conclusion Ġand ĠFuture ĠWork </s> In Ġthis Ġpaper , Ġwe Ġincorporated Ġsub word Ġinformation Ġ( simple Ġn - gram s Ġand Ġun super vised Ġmorp hem es ) Ġinto Ġthe ĠLex V ec Ġword Ġembed ding Ġmodel Ġand Ġevaluated Ġits Ġimpact Ġon Ġthe Ġresulting ĠIV Ġand ĠO OV Ġword Ġvectors . ĠLike Ġfast Text , Ġsub word ĠLex V ec Ġlearns Ġbetter Ġrepresentations Ġfor Ġrare Ġwords Ġthan Ġits Ġword - level Ġcounterpart . ĠAll Ġmodels Ġgenerated Ġcoherent Ġrepresentations Ġfor ĠO OV Ġwords , Ġwith Ġsimple Ġn - gram s Ġdemonstrating Ġmore Ġrobust ness Ġthan Ġun super vised Ġmorp hem es . ĠIn Ġfuture Ġwork , Ġwe Ġwill Ġverify Ġwhether Ġusing ĠO OV Ġrepresentations Ġin Ġdownstream Ġtasks Ġimproves Ġperformance . ĠWe Ġwill Ġalso Ġexplore Ġthe Ġtrade - off Ġbetween Ġsemantics Ġand Ġsyntax Ġwhen Ġsub word Ġinformation Ġis Ġused .