Linking, Optionality, and Ambiguity in Marathi*
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Linking, Optionality , and Ambiguity in Marathi Ash Asudeh November 7, 2000 1 Introduction Optimality theory (OT ; Prince and Smolensky 1993) has in a short time proven influential in theoretical phonology , and to a lesser degree in theoretical syntax. One of the main attractions of the theory is its strong commitment to typology . The basic hypothesis is that there is a set of universal constraints and languages differ only in how they rank the constraints. Since constraints are violable, 1 this will result in the same input having a different optimal expression in different languages. The fact that constraint reranking is the only difference between languages is a very important assumption in OT and is necessary for its explanatory scope (Smolensky, 1996b; Bresnan, 2000), its learnability proofs (T esar and Smolensky, 1998), and most importantly for typology . Once we assume “richness of the base” — as this assumption is often referred to — a natural typology arises. If there are n constraints in a strict ordering then there are n! different ways of ranking them. Since languages differ only in these rankings, it follows then that we predict n! languages. This is called “factorial typology”. Factorial typology is an important, automatic consequence of optimality-theoretic grammars. It is this property that truly sets OT grammars apart from ones in other generative frameworks. However, expressing something easily in a framework often comes at the cost of complicating the formalization of certain other facts. Frameworks in which constraints are inviolable can deal with ambiguity and optionality fairly well. If there are multiple ways of satisfying constraints, then there is optionality (in production) or ambiguity (in comprehension). Similarly , these frameworks have no significant problems with ineffability . This latter term refers to the fact that certain inputs to a grammar have no expression. In frameworks with inviolable constraints, these inputs simply violate one or more constraints and are therefore rejected by the grammar . However, it is precisely the properties of ineffability , ambiguity , and optionality that standard Optimality Theory has trouble expressing. In this paper, I take steps to accommodate ambiguity and optionality in OT syntax. In particular I examine option- ality in linking arguments to grammatical functions in Marathi and give an OT -LFG account of the relevant facts. 2 In doing so, I will illustrate four points. The first is that linking can be achieved with a small set of cross-linguisically plausible, violable constraints. Second, optionality can be captured in OT by modifying the architecture of the theory only slightly . Third, in comprehension directed optimization, the same OT constraints that are used to capture the linking optionality in production can also capture the resulting ambiguity in the Marathi strings that correspond to the This work was supported in part by SSHRC Doctoral Fellowship 752-98-0424 and was completed as a Research Fellow at UCSC, where I was kindly sponsored by Jim McCloskey. I am also very grateful to the following people for discussion, comments, and suggestions: Farrell Ackerman, Judith Aissen, Arto Anttila, David Beaver, Joan Bresnan, Miriam Butt, Ashwini Deo, Alan Gous, Frank Keller, Paul Kiparsky, Chris Manning, Line Hove Mikkelsen, John Moore, Peter Sells, the participants in the Stanford OT W orking Group, and the joint Stanford/UCSC Optimality Theory Syntax class. I would like to particularly thank Ida T oivonen, who has influenced my thoughts on OT and optionality through many long discussions. I accept sole responsibility for any remaining errors. 1 It would be more precise to say that a form can be grammatical and still violate constraints. 2 OT -LFG is a version of OT syntax which uses Lexical Functional Grammar (Bresnan, 1982; Dalrymple et al., 1995) for its representations and as its theory of GEN (see (1) below). 1
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Transcript of Linking, Optionality, and Ambiguity in Marathi*
Linking, Optionality, andAmbiguity in Marathi�
Ash Asudeh
November7, 2000
1 Introduction
Optimality theory(OT; PrinceandSmolensky1993)hasin a shorttime proveninfluential in theoreticalphonology,andto a lesserdegreein theoreticalsyntax. Oneof the main attractionsof the theory is its strongcommitmenttotypology. Thebasichypothesisis thatthereis asetof universalconstraintsandlanguagesdiffer only in howtheyranktheconstraints.Sinceconstraintsareviolable,1 thiswill resultin thesameinputhavingadifferentoptimalexpressionin differentlanguages.Thefact thatconstraintrerankingis theonly differencebetweenlanguagesis a very importantassumptionin OT andis necessaryfor its explanatoryscope(Smolensky,1996b;Bresnan,2000),its learnabilityproofs(TesarandSmolensky,1998),andmostimportantlyfor typology. Oncewe assume“richnessof thebase”— asthisassumptionis oftenreferredto — anaturaltypologyarises.If therearen constraintsin astrict orderingthentherearen! differentwaysof rankingthem. Sincelanguagesdiffer only in theserankings,it follows thenthat we predictn!languages.This is called“factorial typology”.
Factorialtypology is an important,automaticconsequenceof optimality-theoreticgrammars.It is this propertythat truly setsOT grammarsapartfrom onesin othergenerativeframeworks.However,expressingsomethingeasilyin a frameworkoftencomesat thecostof complicatingtheformalizationof certainotherfacts.Frameworksin whichconstraintsareinviolablecandealwith ambiguityandoptionalityfairly well. If therearemultiple waysof satisfyingconstraints,thenthereis optionality (in production)or ambiguity(in comprehension).Similarly, theseframeworkshaveno significantproblemswith ineffability. This latter termrefersto thefact thatcertaininputsto a grammarhaveno expression.In frameworkswith inviolableconstraints,theseinputssimplyviolateoneor moreconstraintsandarethereforerejectedby the grammar. However,it is preciselythepropertiesof ineffability, ambiguity, andoptionalitythatstandardOptimalityTheoryhastroubleexpressing.
In thispaper,I takestepsto accommodateambiguityandoptionalityin OT syntax.In particularI examineoption-ality in linking argumentsto grammaticalfunctionsin MarathiandgiveanOT-LFG accountof therelevantfacts.2 Indoingso, I will illustratefour points. Thefirst is that linking canbeachievedwith a small setof cross-linguisicallyplausible,violableconstraints.Second,optionalitycanbecapturedin OT by modifyingthearchitectureof thetheoryonly slightly. Third, in comprehensiondirectedoptimization,the sameOT constraintsthat areusedto capturethelinking optionalityin productioncanalsocapturetheresultingambiguityin theMarathistringsthatcorrespondto the
�Thiswork wassupportedin partby SSHRCDoctoralFellowship752-98-0424andwascompletedasaResearchFellow atUCSC,whereI waskindly sponsoredby JimMcCloskey.I amalsovery gratefulto thefollowing peoplefor discussion,comments,andsuggestions:FarrellAckerman,JudithAissen,Arto Anttila, DavidBeaver,JoanBresnan,Miriam Butt, Ashwini Deo,Alan Gous,FrankKeller, PaulKiparsky,ChrisManning,LineHove Mikkelsen,JohnMoore, PeterSells, the participantsin the StanfordOT Working Group,andthe joint Stanford/UCSCOptimality TheorySyntaxclass.I would like to particularlythankIdaToivonen,whohasinfluencedmy thoughtsonOT andoptionality throughmanylongdiscussions.I acceptsoleresponsibilityfor anyremainingerrors.
1It would bemorepreciseto saythata form canbegrammaticalandstill violateconstraints.2OT-LFG is aversionof OT syntaxwhich usesLexical FunctionalGrammar(Bresnan,1982;Dalrympleetal., 1995)for its representationsand
asits theoryof GEN (see(1) below).
1
winningcandidatesin production.Fourth,thisOT approachto linking hasinterestingimplicationsfor Dowty’s theoryof proto-roles.
In section2, I briefly presentthebasicarchitectureof standardOptimalityTheoryandhowit relatesto ineffability,ambiguity, andoptionality. Then,in section3, I discussfour possiblewaysof capturingoptionalityin OT.3 In section4, I presentthefactsaboutoptionalityin Marathilinking andreviewJoshi’s (1993)LFG accountof thesefacts.Then,in section6, I presentan Optimality Theoryaccountthat is considerablysimpler in somerespects,althoughit alsousesLFG andthusbenefitsfrom its formalrigourandrepresentationalclarity. I goonto illustratehowtheOT analysisdealswith ambiguityin thecomprehensiondirection. Lastly, I discusssomenewinsightsinto proto-roletheorythatresultfrom thisaccount.
2 Ineffability , Ambiguity , and Optionality in OT
Thebasicarchitectureof OptimalityTheoryis asfollows (PrinceandSmolensky,1993;Bresnan,2000):
(1) Inputs Theinputsto thegrammarareuniversal.Forsyntax,theinputsareusuallyassumedto besemanticformsof somekind (Smolensky,1998).
GEN A generatorfunctionfrom aninput to a setof candidateswhich thegrammarwill evaluate.
EVAL An evaluationfunctionfrom thesetof candidatesto a winningoutput.It consistsof:
1. A universalconstraintset.
2. A languageparticularrankingof theseconstraints.
3. An algorithmfor harmonicordering: the optimal/mostharmonic/leastmarkedcandidate(= theoutputfor agiveninput) is onethatbestsatisfiesthetoprankedconstraintonwhichit differsfromits competitors.(Bresnan,2000;Smolensky,1996a)
Thelastpoint is themostimportantone:OT grammarswill pick themostoptimalcandidate. As Pesetsky(1997)putsit, anOT grammaronly pickswinnersandit will alwayspick awinner. However,therearecertaincasesin whichthereshouldbe no winnersbecausethe input is “ineffable” (i.e. it hasno acceptableoutput). The inability of OTto dealwith ineffability led manyto initially rejectit asa theoryof syntax,but stepshavebeentakento addressthisproblem.Smolensky(1998)presentsanOT accountof ineffability basedon thenotionof production/comprehensionchains. This notion is basedon the ideaof dealingwith productionandcomprehensionusingthe sameconstraintranking.Productiontakesa semanticinputandgivesasyntacticparseasanoutput,whereascomprehensiontakesthestringcorrespondingto thesyntacticparse(i.e. the“overt” partof theproductionoutput)andgivesa semanticformasanoutput.Theoutputof comprehensionis thesametypeof form thatis theinput for production.We canthustalkaboutbidirectionaloptimization4 usinga production/comprehensionchain:5
(2) a. /I/ �production! [S] �overt part! “O” �comprehension! /I0/
b. If /I0/ = /I/, then/I/ is expressible;If /I/ is notexpressibleit it is ineffable.(Smolensky,1998)
3A fifth way,which hasnot beendiscussedmuchin theliterature(for example,it is notmentionedin Muller’s (1999)ratherthoroughoverviewof optionality in OT), is to useunderspecification(in thecandidates,not theinput). I feel thatcandidateunderspecificationhasseverefoundationalproblemsin OT (seeArtstein1998for a generaldiscussion,aswell asKuhn 2000for a discussionspecificto OT-LFG), but this is not theplacetogive theseissuesserioustreatment,which theydeserve.Therefore,I leaveunderspecificationout of this discussion,asI planto examineits role inambiguityandoptionalityandin OT generallyin futurework.
4ForextensiveOT-LFG analysesusingbidirectionaloptimization,seeLee(2001)andDonohue(1999).5I is thesemanticinput; [S] is thesyntacticparse;“O” is thestring;I0 is theoutputof comprehension,but is alsothesametypeof objectasthe
input in production.
2
Oncewe take this approachinto account,althoughthe productiongrammarstill picks a winner, we can formallycapturethe ideathat not all winnersareexpressible.Thus,bidirectionaloptimizationaddressesthe chargethat OTcannotdealwith ineffability andremovesa majorobstaclein adoptingit asa frameworkfor syntaxresearch.
This still doesnot addressthe fact thatOT picksonly onewinner.6 But, therearesituationsin which morethanonecandidateshouldbeselected.Thisoccurswhenthereis eitheroptionalityor ambiguity. Thelattercaseis commonin semanticcomprehension;anexampleis providedby quantifierscopeambiguitysuchasin thefollowing sentence,whichhastheinterpretationsin (3a)and(3b):
(3) Everystudentreada book.
a. Foreverystudentx, thereis abooky suchthatx ready.(widescopeuniversal)
b. Thereis a booky suchthatfor everystudentx, x ready.(narrowscopeuniversal)
An OT grammarthatpicksonly oneof (3a)or (3b)hasfailed to getthefactsright.This problemis not particularto semanticcomprehension.It also occursin phonologicalcomprehension.In
somedialectsof North AmericanEnglish, the following phoneticform is ambiguousbetweenthe two underlyingphonologicalformsin (4a)and(4b).
(4) [ôAjR@ô]
a. /ôAjd@ô/ (= “rider”)
b. /ôAjth@ô/ (= “writer”)
Again,a properlydescriptivegrammarshouldselectboththe(a)and(b) forms.In optimality-theoreticsyntax,ambiguitycanbecharacterizedasa situationin which theconstraintsshouldpick
more thanonesemanticcandidateasoptimal (wherethe semanticcandidatesarethe inputs to productionand theoutputsof comprehension;seefootnote5). Sinceoptionality canbe characterizedasa situationin which an inputhasmorethanoneoptimal outputcandidateaswell, it is just the flip sideof ambiguity. That is, ambiguityoccurswhen thereis morethanoneoptimal candidatein the comprehensiongrammarandoptionality occurswhen thereis morethanoneoptimal candidatein the productiongrammar. Although the focusof this paperis optionality, theideaspresentedherecanbecarriedoverto dealwith ambiguity. In thenextsection,I presentseveralwaysto handleoptionalityandambiguityin OT.
3 Four Approachesto Optionality
Muller (1999)presentsa concisebut thoroughoverviewof optionality in OT. He classifiesproprosalsto dealwithoptionality into four types: pseudo-optionality, neutralization, true optionality, andconstraintties. I will opt for avariantof thelastapproach,butfirst I will discusstheotherthreeapproachesandthereasonsfor rejectingthem.
3.1 Pseudo-optionality
In the pseudo-optionalityapproach,eachoption is the winner of a separateoptimizationcompetition. Optionalityas suchdoesnot exist. Muller pointsout that this predictsthat the variousoptionswill neverbe in competition,which is too strong. Optionality often breaksdown in certaincontexts,but pseudo-optionalitycannotget only onealternantin suchcases,becausethealternantsarenot in competitionby hypothesis.Pseudo-optionalityapproacheswill overgeneratein suchcases.
Oneof Muller’s (1999)exampleswill makethingsclearer. ConsiderEnglishcomplementizerdrop:6Therecan,in principle,becandidateties,in which casebothcandidateswill win, but seesection3.3for criticism of this approach.
3
(5) a. I think thatJohnis a fool.
b. I think Johnis a fool.
Leavingdetailsaside,thepseudo-optionalityapproachwouldhaveeachof thesecandidatesbethewinnerfor distinctinputs.But, thecomplementizercannotalwaysbedropped(Muller, 1999):
(6) a. It surprisedmethattheearthis round.
b. * It surprisedme theearthis round.
On this approach,it is impossibleto ruleout (6b),asthecandidatewith thecomplementizerpresentandtheonewiththecomplementizerabsentareeachthewinnerof theircompetition.Therefore,it is notpossiblefor (6a)to block(6b)andthegrammarovergenerates.
3.2 Neutralization
The secondproposalis neutralization.On this approach,the optionsalsobelongto different competitions,but incertaincontextsa high-rankingconstraintwill resultin thesamesurfacestring beingselectedin eachcandidateset.Thus, the differencebetweenthe two competitionsis neutralized,as the candidatethat wins in eachhasthe samestringyield. Muller reviewstheneutralizationapproachto Englishcomplementizerdropgivenby Bakovic andKeer(Bakovic, 1997;Keer and Bakovic, 1997;Bakovic andKeer, to appear). I will considerthe versionpresentedinBakovic andKeer(to appear),asthis is themostrecentstatementof thisapproach.
Bakovic and Keer makethreekey assumptionsin their analysis. The first is that embeddedCPsand IPs aredistinguishedby thefeatureCOMP. A CPis marked[+ COMP] andanIP is marked[� COMP]. Second,theyfollowDoherty(1993)andGrimshaw(1997)in assumingthatacomplementclauseis aCPif it hasanovertcomplementizerandan IP if it doesnot. Third, theyassumethatanembeddedclausein the input to GEN canbe freely specifiedas[+ COMP] or as[� COMP].
The OT analysisthey proposeis quite simpleandelegant. It involvesonly the two basicOT constrainttypes:faithfulnessandmarkednessconstraints.Thefaithfulnessconstraintis basedon the feature[� COMP] (Bakovic andKeer,to appear:(6)).
(7) FAITH [COMP]: Theoutputvalueof [COMP] is thesameastheinputvalue.
Themarkednessconstraintsfall into two basiccategories.ThereareconstraintsthattargetCP(theMARK-CPfamily)andonesthattargetIP (theMARK-IP family).7
Theanalysisworksasfollows. If FAITH [COMP] outranksMARK-XP, whereXP refersto eitherCPor IP, thenthewinneris thefaithful candidate,asillustratedhere:8
(8) Complementizeroptionality: FAITH [COMP] � MARK-XP
a. Input: [+ COMP] FAITH [COMP] MARK-CP MARK-IP
a. ☞ CP *b. IP * ! *
b. Input: [� COMP] FAITH [COMP] MARK-CP MARK-IP
a. CP *! *b. ☞ IP *
7In theirpapers,Bakovic andKeerspellout theactualconstraintsthatfall into eachfamily of constraints.TheMark-IPandMark-CPconstraintsin Bakovic andKeer(to appear)areessentiallythoseusedin thecomplementizerdropanalysisof Grimshaw(1997).
8All tableauxin this sectionareadaptedfrom Bakovic andKeer(to appear).
4
However,if eithertypeof markednessconstraintis rankedhigherthanthefaithfulnessconstraint,we getneutral-ization.Thatis, nomatterwhethertheinputis marked[+ COMP] or [� COMP], thesamecandidatewins. If MARK-IPoutranksFAITH [COMP], thenthecomplentizeris obligatory.
(9) Complementizerobligatoriness:MARK-IP� FAITH [COMP]
a. Input: [+ COMP] MARK-IP FAITH [COMP]
a. ☞ CPb. IP * ! *
b. Input: [� COMP] MARK-IP FAITH [COMP]
a. ☞ CP *b. IP * !
If MARK-CPoutranksFAITH [COMP], thenthecomplementizeris absent.
(10) Complementizerabsence:MARK-CP� FAITH [COMP]
a. Input: [+ COMP] MARK-CP FAITH [COMP]
a. CP * !b. ☞ IP *
b. Input: [� COMP] MARK-CP FAITH [COMP]
a. CP * ! *b. ☞ IP
In this fashion,Bakovic andKeerget theright resultsfor thecasestheyexamine,lendingsomeempiricalbite to theneutralizationapproach.
But, therearevariousargumentsagainstboth theparticularanalysisthatBakovic andKeerpresentandtheneu-tralizationapproachin general.I will startwith consideringtheproblemswith this particularanalysis.First, thereisno independentmotivationfor thefeature[� COMP] beyondits role in theanalysisof complementizerdrop.Second,this featureis purelya diacritic to indicatewhethersomethingis a CP or an IP. That is, CPsare[+ COMP] andIPsare[� COMP]. Therefore,thefeatureis completelyunnecessary, sincethecategoryinformationencodestherequireddistinction.However,categoryinformationis notusuallyconsideredto bein theinput for asyntacticOT competition.Thisbringsusto thethird objection:theinput is standardlyassumedto beasemanticform of somekind, whetherit isa predicatelogic argumentstructure(Grimshaw,1997),or a partially specifiedLFG functionalstructure(Bresnan,toappear).If this is thecase,thenthepurelyformal feature[� COMP] shouldnotandcouldnotoccurin theinput.
Therearealsoproblemswith theneutralizationapproachin general(Muller, 1999).Fundamentally, thisapproachincreasesthe complexityof inputs,candidatesets,andcandidatecompetition. First, becauseinputscandiffer onlyon someformal feature,therewill bemanyminimally different inputs. As a directconsequence,therewill bemorecandidatesetsto consider. Second,the candidatesareallowed to vary from their inputs indiscriminately(at leastwith respectto theseformal features).So, the constraintson GEN mustbe muchweakerthanstandardlyassumedin OT syntax,allowing it to generateall kindsof candidatesand“everythingcompeteswith everythingelsein eachcompetition”(Muller,1999:7). Third, thisapproachfostersspuriousambiguitieswheninputsareneutralized.Foronecompetitionthe faithful candidateis optimal,while in theothercompetition(which differs only for thevalueof theinput featurein question),theunfaithful candidatewins. That is, whenfacedwith a neutralizedsentence,a languagelearnercannotdeterminewhat thespecificationof the formal featureis in the input. As neutralizationis a commonissuein phonology,PrinceandSmolensky(1993:192)proposethemechanismof ‘LexiconOptimization’asasolutionfor the learnabilityproblem. In OT syntaxthis term is not appropriate,so Muller (1999:8) coinsthe moregeneralterm ‘Input Optimization’. Informally, theway this works is that the languagelearnerselectsthe input for themost
5
harmonicoutputasthecorrectinput. But, asMuller pointsout, this introducesa notionof second-orderoptimizationinto thetheory:InputOptimizationis selectingthebestcompetitionin a setof competitions,whereeachcompetitionis theresultof anoptimizationovera candidateset.This is quitecomplexfrom botha computationalandlearnabilityperspective.
3.3 True Optionality
A third way to selectmultiple winnersin OT is by postulatingtrueoptionality, wherethis meansmultiple candidatesthathaveexactlythesameconstraintprofile. This is theapproachthatGrimshaw(1997)usesto accountfor Englishcomplementizerdrop. Basically, in the caseswherethe complementizeris optional,neitheroptimal candidatevio-latesany of the constraints,or elsebothcandidatesviolate exactlythe sameconstraintsthe samenumberof times.Schematically, thecompetitionlookslike this:9
(11) IP andCPpropositionalcomplements
C1 C2 C3 C4 C5
a. ☞ think [CP that[IP sheleft ]] *b. ☞ think [IP sheleft ] *
Bothcandidate(a)andcandidate(b) areequallyoptimalby definition,astheyhaveidenticalconstraintprofiles.Thus,thereis noway for anOT grammarto pick betweenthem.
Thisapproachhastheadvantagethatit introducesnoextraneousmechanismsor featuresinto thetheory. But, therearetwo strongargumentsagainstits adoption.First, it is veryunlikely10 thatthetwo candidateswill remaintied in alargeOT grammarwith manyconstraints.If anyconstraint,nomatterhow lowly ranked,is violatedby onecandidatebutnottheother,thentheoptionalitywill disappear. It wouldseemto beaninsurmountablydifficult taskto fine-tuneagrammarto thepoint thatnoconstraintdifferentiatesjust thecandidatesrequiredfor optionality. Similarly, a languagelearnerwouldhaveto receiveno informationat all throughouttheentirecourseof acquisitionthatwoulddifferentiatethesetwo candidatesaccordingto evenjustoneconstraint.
The secondargumentagainstthis approach,presentedin Bakovic and Keer (to appear),is that true optional-ity makesstrangetypologicalpredictions. If we assumethat the inputsandcandidatesareuniversal11 (PrinceandSmolensky, 1993;Smolensky, 1996b)andthat theonly locusof typologicalvariationis constraintranking,thenthe
9I glossoverGrimshaw’sactualconstraints,asthedetailsof heranalysisarenot necessaryfor theargumentsI give here.Thetableauis basedon Grimshaw(1997:411)andMuller (1999:4).
10In a previousversionof this paperI presentedan argumentagainsttrue optionality basedon simpleprobability theory. Therearevariousproblemswith the argument(as pointedout to me by Chris Manning and Alan Gous),but it is still somewhatinstructive. Given a constraint,Constraint1 , and two candidates,Candidate1 andCandidate2 , thereare two possibleoutcomes:either Candidate1 and Candidate2 are tied forConstraint1 (bothcandidateshavea no marksfor this constraintor an identicalnumberof marks),or thereis no tie. In general,theprobability oftwo candidatestying on n constraintsis ptie(n) = 1
2nIt is easyto tell that,evenwith asmallnumberof constraints,theprobabilityof acandidate
tie (i.e. trueoptionality) is very low. For example,with just twentyconstraints,theprobabilityof a tie is 1
1048576.
Therearethreeproblemswith this argument,though.First, it requiresanassumptionof independencebetweenconstraintsandthis is obviouslyanidealization.In particular,sincetherewill beconflicting faithfulnessandmarkednessconstraintsin OT grammars,satisfactionof oneconstraintwill often result in violation of another.A relatedproblemis that not all of the constraintsin a grammarwill apply to all candidates.However,evenif wesubtractthenumberof constraintsthat in principlecannotapplyto aninput from thetotal numberof constraintsandremovemarkednessandfaithfulnessconstraintsthat are in conflict for the input in question,we will still be left with a numbersufficiently largeto makea tie veryimprobable.
Second,theargumentassumesthat thereis a uniform distributionover ties in theprobabilitymodel.That is, it assumesthat it’s just aslikely onanygivenconstraintfor two candidatesto tie asit is for themnot to (this is like assumingthatthecoin is fair whencalculatingprobabilitiesfor cointosses).This is surelythedefaultassumption,givenno furtherdata.But, it is thevery lack of this furtherdatathat is thethird andworstproblem.Thereis noprobabilitymodelhere,aswehaveno way of replicatingtheexperiment.Althoughwecancalculateprobabilitiesfor thecompetitionitis unclearhow theprobabilitiesmodeltherealworld. That is, theresultsof theexperimentarepurely theoretical,andit is difficult to seehow theycanbetestedempirically.
11I amnot necessarilyadvocatingsuchanassumptionhere.Ultimately, I expectbothassumptionsto beuntenablein a morematureversionofthetheory.
6
trueoptionalityapproachpredictsthatwhateveroptionalitythereis in onelanguageshouldexistin all languages.Thereasonis simple:if two candidatesarenotdistinguishedby theirconstraintprofiles,thennomatterhowtheconstraintsareorderedtheywill still be indistinguishable;sincetheonly differencebetweenlanguagesis constraintranking,byhypothesis,thetwo candidateswill beequallyoptimalin all languages.Of course,in anygivenlanguageothercandi-datescouldbemoreoptimalthaneitherof thetwo consideredhere.However,thesetwo argumentsmakeit clearthatthetrueoptionalityapproachis notagoodsolution,becauseit raisesmoreproblemsthanit solves.
3.4 Constraint Ties
Theapproachesconsideredsofar assumea standardOT grammarwhoseconstraintsetformsa total order. Thefinalapproachto optionality I consideris thatof allowing constraintties,12 which effectivelyrelaxestherequirementof atotalorderby permittingequallyordered(or unordered)constraints(i.e. constraintsthatare“tied” for apositionin theordering).
3.4.1 Partial Orderings
Oneof thepropertiesof a total orderis thatof connectedness:everyconstraintmustberankedrelativeto everyotherconstraint(Anttila, 1997a).If we do not requirethis property, thenwe cancharacterizeanOT grammarasa partialorder. In this casesomeof the constraintswill not be rankedrelativeto someothers. A schematicview of a totalorderingandapartialorderingof threeconstraintsis givenin (12):
(12) Total Ordering PartialOrderingA
B
C
A
B C
Thetotal orderingstatesthatA � B � C, whereasthepartialorderingstatesthatA � B andthatA � C. However,in thepartialorderingthereis noorderingbetweenB andC. It is neitherthecasethatB � C nor thatC� B.
Assumingnconstraintsthatarepartiallyordered,n! possibletotalorderingsof justtheseconstraintscanbederived.In this case,we getthetwo total orderingsA � B � C andA � C� B. Insofarasthetotal orderingsderivedwillconstitutedifferent constraintrankings,different candidatescan be selectedas optimal on different resultingtotalorders.Thus,partialordersoffer themostgeneralmethodfor relaxingthetotal orderingon constraintsandderivingsomeoptionalityin output.
Therearetwo majorproblemswith generalpartialorders.To understandthefirst problem,considerapartialorderlike thefollowing:13
(13) A
B C...Z
Thispartialorderingcorrespondsto twenty-fivetotalorderings,whichcanberepresentedwith thesethreeschemas:12This is alsothe mostwidely utilized approachto optionality. RelevantreferencesincludeAnttila (1997a,b);Boersma(1997,1998,to appear
a); BoersmaandHayes(1999);Pesetsky(1997,1998);PrinceandSmolensky(1993).SeeMuller (1999)for futher referencesanddiscussion.13TheelidebetweenC andZ indicatesa total orderingof twenty-fourconstraints,with C asthehighest-rankingconstraint.
7
(14) a. A � B � C� . . .� Z
b. A � C� . . .� B � . . .� Z
c. A � C� . . .� Z � B
Theproblemis evident:sincethetotalorderingfrom C to Z mustbemaintained,thevariousresolutionsgiveradicallydiffering importanceto theconstraintB. Thetwo extremecasesareprovidedby (14a)and(14c). In (14a),B is veryhighly rankedandcanonly beviolatedin orderto satisfyA. However,in (14c),thesameconstraintB is rankedverylow andwill notplayasignificantrole in selectingacandidate.This is averyseriousconceptualproblemwith partialorders.Furthermore,it will makemodellingoptionalityverydifficult: if thepartialorderingis betweenhighly-rankedconstraintsin a largeconstraintset,thetwo total orderswill beradicallydifferent,mostlikely yieldingnooptionalityafter all. Thus,thereis not only a conceptualproblem,but we alsoencounteragainthe problemof constructingagrammarwhereotherconstraintsdonot interferein theoptionality, asis thecasewith thetrueoptionalityaccount.
Thesecondproblemwith thisapproachis thatthereis nonon-exponentiallearningalgorithmfor partiallyorderedOT grammars(BoersmaandHayes,1999;Boersma,to appearb; Kager,1999).NeitherTesarandSmolensky’s(1998)norBoersmaandHayes’s (1999)learningalgorithmworksfor suchgrammars.In fact, theformerlearningalgorithmonly worksfor totally orderedOT grammarswithout ties(I returnto thispoint in thefollowing sections).As therearelearningalgorithmsfor theotherapproachesI reviewbelow, theyareto bepreferredto partiallyorderedgrammars.
3.4.2 Stratifiable Partial Orderings
Anttila (1997a;1997b)proposesaslightly modifiedversionof partialorderingto handleoptionalityin Finnishgenitiveplurals.14 His approachis essentiallya hybridof totalandpartialorderings:constraintsfall into setswhicharetotallyorderedwith respectto eachother;however,within thesetstherecanbepartialordering.I will notgo into thedetailsof Anttila’sanalysis,asit is quiterich andcomplex,but thethreehighestrankingsetsheproposesaregivenhere.15
(15) SET 1 SET 2 SET 3*X.X * L *H/I
*H * I*L.L
Thisstratifiedpartialorderingcorrespondsto thefollowing twelvetotal orderings:
(16) 1. * X.X � * L � *H � *H/I � * I � *L.L
2. * X.X � * L � *H � *H/I � *L.L � * I
3. * X.X � * L � *H � * I � *H/I � *L.L
4. * X.X � * L � *H � * I � *L.L � *H/I
5. * X.X � * L � *H � *L.L � *H/I � * I
6. * X.X � * L � *H � *L.L � * I � *H/I
7. * X.X � *H � * L � *H/I � * I � *L.L
8. * X.X � *H � * L � *H/I � *L.L � * I
9. * X.X � *H � * L � * I � *H/I � *L.L
10. * X.X � *H � * L � * I � *L.L � *H/I
11. * X.X � *H � * L � *L.L � *H/I � * I
12. * X.X � *H � * L � *L.L � * I � *H/I14Theterm‘stratifiedpartialordering’comesfrom Boersma(to appearb).15Whattheconstraintsmeanis not relevanthere.Thecuriousreaderis invited to consultAnttila’s work.
8
As the partial orderingsareonly within eachset/stratum,this approachdoesnot suffer from the first problemof(general)partialorderingsdiscussedabove.In otherwords,thevarioustotal orderingsareminimally different fromeachotherandnooneconstraintvariestremendouslyin its rankingposition.
Usingthis approach,Anttila (1997a,b)getsvery interestingresultsin boththesynchronicanddiachronicdimen-sionsof grammar. Furthermore,hemakesaccuratepredictionsabouttheactualdistributionof alternantsof thegenitiveplural in Finnishcorpora.I contendthat it is problematicto expectgrammarsto predictactualpercentagesof formsin corpora,asperformancefactorssurelymediatebetweentheformsproducedby thegrammarandformslisted in acorpus.However,supposingthatwe do wish to usethegrammarto predictactualpercentages,we quickly run intoa strangeconsequenceof stratifiablepartialorderings.Supposethat a givencandidateis optimalunderthreeof thetwelveorderingsin (16).Thepredictionis that25%of theformsattestedshouldbethiscandidate.BoersmaandHayes(1999)pointout thattherearecasesof freevariationin whichonevariantis considerablymorefrequentthantheother.An exampleis Englishwordsthatphonemicallyendin /. . .ft,dg@n/. Thesearenormallyrealizedwith no schwaanda syllabic /n/, asin Sweden[swi:dn
"
], but areoccasionallyrealizedwith the schwa,as in [swi:d@n]. Let us assumepurely for the sakeof argumentthat the rarely attestedform occursoncefor everythirty-two timesthe commonlyattestedform does.For a grammarwith threepartially orderedconstraintsets,thefirst andsecondof which containtwo constraintsandthethird of whichcontainsfour constraints,thereare96 (2!�2!�4!) totalorderings.Thus,therarerform wouldhaveto beoptimalon threeof thetotal orderings.Or, for a grammarwith thirty-two constraintsets,eachof whichcontainsonly oneconstraint,therarerform wouldhaveto beoptimalon onetotal ordering.In anycase,wehavea situationin which thefrequencyof occurrenceof theformsproducedby thegrammarpartially determinesthenumberof constraintsin thegrammarandtheir organizationinto strata.This is a very peculiarstateof affairs,asagrammarwith fewerconstraintsshouldin principlebepreferredto a grammarwith moreconstraints,providingtheymakeidenticalpredictionsaboutgrammaticality.
Evenif weputthisconceptualproblemaside,thereis asecondproblemwhichhasto dowith learnability(Boersma,to appearb; BoersmaandHayes,1999;Kager,1999).TesarandSmolensky’s(1998)learningalgorithmcannothandlestratifiablepartialorderings:it eitherfails to acquirethemor it neverterminates(BoersmaandHayes,1999).However,aswe will seein thenextsection,stratifiablepartialorderingsarejust a specialcasein stochasticOptimality Theory,whichusesamodifiednotionof constraintranking,whichassociatesconstraintswith a rankingvalueonacontinuousscale.StochasticOT hasan associatedlearningalgorithm: the GradualLearningAlgorithm (Boersma,to appeara;BoersmaandHayes,1999).Sincethecontinuousrankingapproachavoidsthefirst problemnotedabove(seebelow),is learnable,andcanhandlestratifiedpartialorderingsasa specialcase,it will betheapproachI adopt.
3.4.3 StochasticOptimality Theory
Boersma(1997,1998,to appeara)andBoersmaandHayes(1999)presentamethodologyfor representingoptionalityin astochasticversionof OptimalityTheory. Themethodinvolvesassigningarankingvalueto eachconstraint.Thatis,eachconstrainthasarankingvaluealongacontinuousscaleandatconstraintevaluationtimetherankingof aconstraintis randomlydistributedaroundthis rankingvalue(seefigure 1 below). Theupshotof this is that if two constraintsarecloselyranked,therewill beoptionality in which constraintis rankedhigherthantheother. Thefrequencyof agivenranking(e.g.whetherC1 outranksC2), dependson therankingdifference.If therankingdifferenceis high, theconstraintwith thehigherrankingvaluehasaveryhighprobabilityof outrankingtheconstraintwith thelowerrankingvalue.If therankingdifferenceis 0, eachconstraintwill outranktheotherfifty percentof thetime.
Thus,constraintevaluationis stochasticin this system.Constraintsdo not deterministicallyoutrankothercon-straints,but ratherdo so probabilistically. This probability is calculatedusinga Gaussian(i.e. normal)distribution,andthe formula is not trivial. For the mathematicaldetails,seeBoersma(1997,1998,to appeara). However,theeffectsfor two constraints— C1 andC2 — areillustratedin thefollowing table(Boersma,1997):
(17) r1 � r2 0 1 2 3 4 5 6 7 8 9 10P 0.5 0.46 0.24 0.14 0.079 0.039 0.017 0.007 0.002 7�10�4 2�10�4
9
90 88 86 84 82 80strict lax
C1 C2
Figure1: Overlappingrankingdistributions(BoersmaandHayes,1999:(6))
Thetop line indicatestherankingdifferencebetweentherankingvaluesof thetwo constraints,r1 andr2. Thebottomline indicatestheprobability, P, of thesecondconstraintoutrankingthefirst one.16
If two constraintsarerankedcloselyenough,on differentevaluationsonewill be rankedhigherthanthe other.This yields optionality. However,if two constraintsarerankedfairly far apart,the probabilityof different rankingsbecomessmall. That is, the rankingbecomescloseto obligatory(Boersma,1997). It is importantto realizethat atevaluationtimetherewill alwaysbeatotalordering.Sincetheprobabilityof thefirst constraintoutrankingthesecondis relativeto eachevaluation,in anygivenevaluationtherankingis determinate.
It shouldbeevidentthatstratifiablepartialorderings(Anttila, 1997a,b)aresimplya specialcasein thisapproach.A stratifiedpartialorderingis derivedin a constraintrankingwhereseveralconstraintshaveexactlythesamerankingvalue.Forexample,considerthefollowing schematictableau,whichhastherankingvaluefor eachconstraintindicatedbelowtheconstraint’sname.
(18) C1 C2 C3 C4 C5
150 100 100 100 50...
Bearingin mind that eachconstraintis associatedwith the samenormaldistribution,constraintsC2, C3, and C4,areequally likely to occurin any of the six (3!) possibleorders. The other two constraintswill not enterinto theoptionality, astheir rankingvaluesaretoo far from the constraintsrankedat 100. This is equivalentto a stratifiedpartialorderingwith threestrata:C1 � fC2, C3, C4g � C5.
In my analysisof optionality in Marathi linking (section6), I assignthe optionally rankedconstraintsthe samerankingvalue.SoI effectivelyusea stratifiedpartialordering.Thereasonsfor usingthestochasticOT modelinsteadof a simplestratifiedpartial orderingweregivenat the endof the last section. First, the stochasticmodeldoesnotdependon a direct relationshipbetweenthe numberof constraintsin the grammar(andhow they aredivided intostrata)andthefrequencyof theoptionalforms. In thestochasticmodel,thefrequencyof formsdependsonly on therankingvalueof theconstraintsin question.17 Therefore,on this approachit is possibleto havea simplergrammar,with fewerconstraints,andstill predictthesamefrequencydata.18
Thestochasticmodelalsohasa learningalgorithm— theGradualLearningAlgorithm (GLA; Boersmato appeara;BoersmaandHayes1999)— andgrammarsstatedin thismodelarethereforelearnable.Stratifiedpartialorderingshaveonly beendemonstratedto belearnableby theGLA (Boersma,to appearb). Until a separatelearningalgorithmthatdoesnotdependonastochasticmodelof OT is shownto learnstratifiablepartialorderings,thereis nochoicebutto implementthemusingcontinousrankingandstochasticOptimalityTheory.
Thus,continuously-rankedconstraintsoffer a way to representoptionalitywhich avoidstheproblemsassociatedwith previousapproaches.Theproblemof overgenerationwith thepseudo-optionalityapproachdoesnotarise,because
16Therankingvaluesassignedto constraintsdo nothaveto beintegers;finer grainedprobabilitiescanbeassignedusingpositiverealnumbers.17Another relevantfactor is the standarddeviationof the normaldistributionassociatedwith the constraints,but asthis is the samefor every
constraint,it is aconstant.18I takethis to beaconceptualpoint abouttheundesirabilityof frequencydatadictatingthenumberof constraintsin thegrammar.However,asI
statedin section3.4.2,I donotbelievethatthegrammarshouldeverpredictexactfrequencies,astherewill bemanyinterveningfactorswhich willdistort thefrequencyof formsattested.
10
herethevariousoptionsbelongto thesamecompetition.Theproblemof maintainingidenticalconstraintprofilesinthe true optionality approachdoesnot arisebecausethe candidateswin on different rankingsand do not needtohaveidenticalconstraintprofiles.Thevariousproblemswith theneutralizationapproacharedispensedwith, becausethereis only oneinput andit doesnot requireformal features,aswill bedemonstrated.Lastly, in this sectionI havedemonstratedthatthis approachis thesoundestandmostgeneralway of implementingthenotionof tied constraints,asit is equivalentto stratifiablepartialorderingsasa specialcase,but is alsolearnable.In the following sectionsIreviewthefactsaboutMarathithatI will beaccountingfor, aswell asJoshi’s (1993)insightfulLFG accountof thesefacts. In section6, I presentmy own analysis,which usescontinuously-rankedconstraintsin a stochasticOptimalityTheory.
4 Subject/Object Alternation in Marathi
Marathi19 presentsan interestingcasefor studyingoptionality in Optimality Theory. Certainverbsin this languageshowanalternationin which argumentcanbe realizedasthesubjector object. That is, given two arguments,eachcanbemappedto thesubjectwhile theotheris mappedto theobject. Theverbclassesthatexhibit this propertyarenon-volitionaltransitives,suchas‘saapad
n
e’ (to find) and‘aawad
n
e’ (to like), passivesof underivedditransitives,suchasthepassiveof ‘den
e’ (to give), andpassivesof derivedditransitivecausatives,suchasthepassiveof causativized‘paa
>
dzn
e’ (to feed).20 In thenextsectionI will presentexamplesof all of theseverbsandtheevidencethatJoshiusesto arguefor thealternationof theircoregrammaticalfunctions.
4.1 The Data
Hereareexamples21 of theverbclassesmentionedin theprevioussection.
(19) Non-volitional transitives
a. sumaa-laaSuma-D/A
ekone
pustakbook
milaale.got
Sumagota book.(‘mil
n
e’ (to receive)2 ‘find’ class)
b. sumaa-laaSuma-D/A
aai>tsaamother’s
upadesadvice
patlaa.agreed
Mother’sadvicebecameacceptableto Suma.(‘pat
n
e’ (to acceptmentally)2 ‘like’ class)
(20) Passiveditransitivebaal
aa-laababy-D/A
khaaufood
dilaagiven
gelaa.was
Babywasgivenfood.
(21) Passiveditransitive causativeramaa-laaRama-D/A
dudh
milkpaa
>
dzledrink.CAUS
gele.was
Ramawasfedmilk.19Marathi is anIndo-Aryanlanguagespokenin Maharashtraandadjacentstatesin India. It hasroughly64,000,000speakers.Ethnographicdata
from Ethnologue(http://www.sil.org/ethnologue/countries/Inda.html#MRT;checked17.03.99).20SeeappendixA for a list of verbsin theseclasses.21All Marathi examplesare takenfrom Joshi(1993) and I haveusedher transcriptions,with one exception. Shegives variouscasefeature
transcriptions(seefootnote22), for thesuffix -laa, whereasI normallyglossit asdative/accusative,sincethedistinctionsareneverrelevantin thispaper.SeeappendixD for examplecitation informationandappendixB for anindexof relevantglossabbreviations.
11
Joshi(1993)separatesthenon-volitionaltransitivesinto the‘find’ class(19a)andthe‘like’ class(19b).
4.2 Evidencefor the Subject/Object Alternation
Joshi(1993:37–44)lists threesyntactictestsfor grammaticalsubjecthoodin Marathi: the conjunctionreductiontest, the prenominalpresentparticipial relative test, and the un clausecontrolleetest. Sheusesall threeteststodemonstratethesubject/objectalternationin theverbclassesundercosideration,but in this sectionI will only reviewtheconjunctionreductiontest.
Theconjunctionreductiontestis notsolelya testfor subjects,but ratherdistinguishesbetweengrammaticalfunc-tions in general. The phenomenoncan be characterizedas follows: if two clauseswith coreferentargumentsareconjoined,the coreferentargumentin the secondclausemustbe null, but only if the two argumentshavethe samegrammaticalfunctionandhavethesamecasemarking(Joshi,1993:37–38).22 For illustrationof theserequirementsonconjunctionreductionthereaderis invited to consultJoshi(1993).I will showthatonly subjectscangapsubjects,asthiswill beimportantin demonstratingthesubject/objectalternationmomentarily.
The following exampleshavethe propertiesrequired.23 Example(22) showsthat subjectscangapsubjectsandmustdo so if the casesmatch,example(23) showsthat subjectscannotgapobjects,andexample(24) showsthatobjectscannotgapsubjects.
(22) a. [alkaa-niAlka-ERG
mini-laamini-D/A
maarle]hit
panbut
[niilimaa-laaNilima-D/A
wihiritwell-in
d
hakalle].pushed
Alkai hit Mini, butØi pushedNilima in thewell.
b. * [alkaa-niAlka-ERG
mini-laamini-D/A
maarle]hit
panbut
[tinishe-ERG
niilimaa-laaNilima-D/A
wihiritwell-in
d
hakalle].pushed
Alkai hit Mini, butshei pushedNilima in thewell.
(23) a. * [>
dzaambhl
i-ciJamun-GEN
pikleliripe
phal
efruits
khaalidown
pad
li]fell
aan
iand
[raam-niRam-ERG
gol
aacollect
keli].did
[Ripe fruits of theJamuntree]i fell on theground,andRamcollectedØi.
b. [>
dzaambhl
i-ciJamun-GEN
pikleliripe
phal
efruits
khaalidown
pad
li]fell
aan
iand
[tithose
raam-niRam-ERG
gol
aacollect
keli].did
[Ripe fruits of theJamuntree]i fell on theground,andRamcollectedthemi.
(24) a. * [pustakebooks
raam-niram-ERG
t
eblaa-wartable-on
t
hewli]kept
panbut
[khaalidown
pad
l
i].fell
Ramputbooksi on thetablebutØi fell down.
b. [pustakebooks
raam-niram-ERG
t
eblaa-wartable-on
t
hewli]kept
panbut
[tithey
khaalidown
pad
l
i].fell
Ramputbooksi on thetablebut theyi fell down.
Theseexampleshavedemonstratedhow conjunctionreductionfunctionsin Marathi. Next I will showhow this testprovidesevidencefor thesubjectalternation.
4.3 Subject Alternation
First,theconjunctionreductiontestwill beusedto demonstratethateitherargumentin non-volitionaltransitives(‘like’and‘find’ verbs)canbethegrammaticalsubject.This follows from conjunctionreductionbecause,asdemonstrated
22 It is importantto realizethatthetwo argumentshaveto havethesamecasemarking,but canhavediffering casefeatures,or abstractcase.Forexample,the dative/accusativecaseandthe ability casein Marathi arebothmarkedwith the suffix -laa, but Joshi(1993:18–30)arguesthat theymustbedistinguishedasseparatecases.
23In all theseexamples,thecase-matchingrequirementhasbeencontrolledfor.
12
in (22), “when thesubjectsof conjoinedclausesarecoreferentandhavematchingcases,thesecondsubjectis null”(Joshi,1993:53). Thefollowing examplesillustratethateitherargumentof a non-volitionaltransitivecanbegappedin thesecondconjunct.
(25) ‘like’ class
a. Dative/accusativeargument gapped[rajat-laaRajat-ABIL
aai-laamother-D/A
ext
ialone
t
aakunleaving
yewawlacome
naahi]able.not
panbut
[hyaathis
citrakaaraa->tsaartist-GEN
maag>tsalast
pradarsanexhibition
khupmuch
aawad
laliked
hota].had
Rajati did not feel like leaving motheralone and coming,but Øi had muchliked the artist’s lastexhibition.
b. Nominative argument gapped[sumaaSuma
godsweet
hasli]smiled
aan
iand
[rajat-laaRajat-D/A
aawad
la].liked
Sumai smiledsweetlyandRajatlikedØi.
Thesesentencesillustratethateitherargumentof thenon-volitionaltransitive‘aawad
n
e’ (to like) canbegappedby acoreferentsubjectin a precedingconjoinedclause.This indicatesthatbothargumentsof a ‘like’ classverbcanserveasthegrammaticalsubject.
Thefollowing sentencesshowthattheargumentsof ‘find’ classverbshavethesameproperties.
(26) ‘find’ class
a. Dative/accusativeargument gapped[raajkumaaraa-laaprince-D/A
waal
wan
t
aatdesert.in
sod
laleft
gela]was
panbut
[paratback
>
dzaay->tsaago-GEN
rastaaway
saapad
laa].found
Theprincei wasleft in thedesert,butØi foundhiswayback.
b. Nominative argument gapped[raajkumaaraa-ciprince-GEN
angt
hiring
tal
yaatlake.in
pad
li]fell
aan
iand
[ekaaone
kol
yaa-laafisherman-D/A
saapad
li].found
Theprince’sringi fell in thelakeandonefishermanfoundØi.
Again,sincethesubjectin thefirst clausecangapeitherof the‘find’ verb’sarguments,thisshowsthateitherargumentcanbethegrammaticalsubject.
Theconjunctionreductiontestcanalsobeusedto demonstratethateitherargumentin a passiveof a ditransitiveor ditransitivecausativecanbethegrammaticalsubject.
(27) Passiveditransitive
a. Dative/accusativeargument gapped[mini-laaMini- D/A
khurcitchair.in
basawlesit.made
gele]was
aan
iand
[ekone
pustakbook
dilegiven
gele].was
Minii wasseatedin a chair andØi wasgivena book.
b. Nominative argument gapped[ekone
pustakbook
khaalidown
pad
le]fell
aan
iand
[mini-laaMini- D/A
dilegiven
gele].was
Onebooki fell downandØi wasgivento Mini.
13
(28) Passiveditransitive causative
a. Dative/accusativeargument gapped[mini-laaMini- D/A
gharihome
neletaken
gele]was
aan
iand
[tethat
dudh
milkpaa
>
dzledrink.cause
gele].was
Minii wastakenhomeandØi fedmilk.
b. Nominative argument gapped[tethat
dudh
milktaaple]warm.became
aan
iand
[mini-laaMini- D/A
paa>
dzledrink.cause
gele].was
Themilki becamewarmandØi wasfedto Mini.
Onceagainasubjectcangapeitherof thetwo argumentsof theseverbs.Therefore,eitherargumentcanbethesubject.In thissection,I presentedJoshi’sevidencethateitherargumentin theverbclasseswith subject/objectalternation
canbe the grammaticalsubject. In the next section,I will showthat eitherargumentcanalsobe the grammaticalobject.
4.4 Object Alternation
Testingfor the object alternationin Marathi is slightly more complexthan testingfor the alternatingsubject. Itinvolvesusinga controlledadjunct,thetaanaaparticipleadverbialclause,in tandemwith conjunctionreduction.Thebasiccharacterizationof the tanaaadverbialis that it hasan obligatorily null subjectwhich canonly be controlledby anargumentwhich hasa non-obliquegrammaticalfunction(i.e. subjectsandobjects).Joshi’s strategyis to useconjunctionreductionto establishthatoneargumentof therelevantalternatingverbis a subjectandthento showthatthe otherargumentcancontrola taanaaadverbialclause.Underthe assumptionthat therecanonly be onesubjectper clausein Marathi, andgiven that only non-obliquescancontrol the gapin theseadverbials,it follows that thecontrollermustthereforebea grammaticalobject.
In thefollowing examples,conjunctionreductionis usedto establishthatoneof thealternatingverb’s argumentsis the grammaticalsubject. At the sametime the taanaacontrol testshowsthat the otherargumentmustbe a coregrammaticalfunction.Sincethereis anuncontroversialsubject,thisotherargumentmustbetheobject.
(29) Non-volitional transitives (‘like’ class)
a. Dative/accusativeobject ascontroller[raajakanyaaPrincess
rajat-laaRajat-D/A
[angholbathkartaanaa]do.PART
disli]saw
aan
iand
[naahisidisappear
>
dzhaali].became
WhileØi=j takinga bathRajati sawtheprincessj andØj disappeared.
b. Nominative object ascontroller[rajat-laaRajat-D/A
raajakanyaaprincess
[angholbathkartaanaa]do.PART
disli]saw
aan
iand
[girafdaarimprisoned
keledone
gele].was
Rajati sawtheprincessj Øi=j bathingandØi wasimprisoned.
In sentence(29a),raajakanyaais gappedunderconjunctionreductionwith thesubjectof naahisi (disappeared).There-fore, raajakanyaais the subjectof disli (saw). Sincerajat canstill control the adverbialkartaanaa, it mustbe thegrammaticalobjectof disli . Likewise,in (29b),rajat-laais coreferentwith thegappedsubjectof girafdaarandmustthereforebethesubjectof disli . Theadverbialkartaanaacanbecontrolledby raajakanyaaandraajakanyaais there-fore thegrammaticalobjectof disli . Thus,eitherargumentof thenon-volitionaltransitive‘like’ verbdisn
e canbethegrammaticalobject.
Thetestyieldsthesameresultsfor ‘find’ verbs,passiveditransitives,andpassiveditransitivecausatives.In eachcase,conjuctionreductioncontrolsfor thesubject,andeitherargumentcanbethegrammaticalobject. Therelevantexamplesfollow, but I will notexplaineachonein detail.
14
(30) Non-volitional transitives (‘find’ class)
a. Dative/accusativeobject ascontroller[kaahisome
morpeacocks
rajat-laaRajat-D/A
[janglaatjungle.in
phir-taanaa]roam-PART
aad
hal
le]came.across
aan
iand
[ekaaone
ksan
bharaa-t-a>tsmoment-in-only
naahisedisappear
>
dzhaale].happened
Rajati cameacrosssomepeacocksj whileØi roamingin thejungle,andØj in a momentdisappeared.
b. Nominative object ascontroller[rajat-laaRajat-D/A
kaahisome
morpeacocks
[janglaatjungle.in
naa>ts-taanaa]dance-PART
aad
hal
le]came.across
panbut
[tcyaancyaathey.GEN
>
dzawalnear
>
dzaawawlego.ABLE
naahi].not
Rajati cameacrosssomepeacocksj whenØj dancingin thejungle,butØi couldnotgonearthem.
(31) Passiveditransitives
a. Dative/accusativeobject ascontroller[aai-nimother-ERG
magawleliordered
pustakabooks
sakaal
-cyaamorning
t
apaalaa-nipost-with
aali]came
aan
iand
[[ saal
etschool.to
>
dzaa-taanaa]go-PART
sumaa-laaSuma-D/A
daakhawlishown
geli].were[Thebooksthatmotherhadordered]i camewith themorningpostandØi wereshownto Sumaj whileØj goingto school.
b. Nominative object ascontroller[sumaa-laaSuma-D/A
madhya-raatrimiddle.of-night
ut
hawlawoken
gela]was
aan
iand
[hiwaal
yaa->tsawinter-of
pahilafirst
barfasnow
[pad
taanaa]falling
daakhawlashown
gela].was
Sumai waswokenupat midnightandØi wasshown[the first snowof winter]j whileØj falling.
(32) Passiveditransitive causatives
a. Dative/accusativeobject ascontroller[dudh
milkgaramwarm
>
dzhaala]became
aan
iand
[raam-laaRam-D/A
[>
dzhoptaanaa]sleep.PART
paa>
dzladrink.CAUS
gela].was
Themilki gotwarmandRamj wasfedØi whileØj goingto sleep.
b. Nominative object ascontroller[raam-laaRam-D/A
bhukhunger
laagli]came
aan
iand
[dudh
milk[garamhot
astaanaa]be.PART
paa>
dzladrink.CAUS
gela].was
Rami becamehungryandØi wasfedsomemilkj while Øj hot.
In the last two sections,I havepresentedJoshi’s evidencethat in Marathi non-volitionaltransitives,passivedi-transitives,andpassiveditransitivecausativeseitherargumentcanbe thegrammaticalsubjectwhile theotheris thegrammaticalobject.Therefore,theseverbclassesdemonstratetrueoptionalityin linking theirargumentsto grammat-ical functions.In thenextsection,I outlinetherelevantaspectsof Joshi’sLFG analysis,beforeturningto my ownOTanalysisin section6.
5 Joshi’sAnalysis
In her thesis,Joshiusesa combinationof Lexical MappingTheory(LMT; BresnanandKanerva1989;BresnanandZaenen1990),which is a componentof LFG, Dowty’s proto-roletheory(Dowty, 1991),andanunorderedargumentstructurerepresentationto giverulesthatprovidepredicateswith their requiredlinking properties.
15
LexicalMappingTheoryprovidestwo syntacticfeatures[� r(estricted)]and[� o(bjective)]which cross-classifygrammaticalfunctions.Joshi(1993:113)assumesthefollowing classification:24
(33) SUBJ: [�r,�o], OBJ: [�r,+o], OBL: [+r,�o](Joshi,1993:113)
In Joshi’s analysis,theLMT featuresof anargumentaredeterminedby theproto-rolesassociatedwith theargu-ment.Theproto-propertiessheassumesare:
(34) Proto-Agent Properties
a. volitional involvementin theeventor state
b. sentience(and/orperception)
c. causinganeventor changeof statein anotherparticipant
d. movement(relativeto thepositionof anotherparticipant)
e. existsindependentlyof theeventnamedby theverb
(35) Proto-Patient Properties
a. undergoeschangeof state/location
b. incrementaltheme
c. causallyaffectedby anotherparticipant
d. stationaryrelativeto movementof anotherparticipant
e. doesnotexistindependentof theevent,or notat all
f. notentailedto possesssentience/perception
(Dowty 1991:572;Joshi1993:80)
I follow Joshiin referringto thesepropertiesasPAa, PAb, PPa,PPb,andsoforth.Proto-propertyPPf is not oneof theoriginal Dowty (1991)properties,but is proposedby Joshi(1993).But, this
propertyis quiteproblematicandI donotadoptit. First, it is notclearwhatits motivationis. Although,asJoshi(1993:90) notes,lack of sentiencemay be a reasonableproto-patientproperty, the entailmentshouldbe non-sentience,asopposedto PAb. ThereasonJoshioptsfor thepresentformulationinstead,is to getcertainfactsaboutMarathilinkingright (see(39)below).Thus,theproblemis a lackof independentmotivation.
Second,thisproto-propertyworkscompletelydifferentlyfrom theotherones.25 In Joshi’s formulation,thisproto-propertywill necessarilybe presentwheneverPAb is absent. In other words, any argumentof a verb that is notentailedto havethe proto-agentpropertyof sentience(PAb) will automaticallyreceivepropertyPPf. It is clear,asDowty (1991)notes,that someproto-propertiesare related,in particularPAc-d andPPc-d,but they are relatedpositively, notnegatively. Forexample,if oneargumentis a causer(PAc), thentheremustalsobeanargumentthatisthecausee(PPc).Thus,thepresenceof thefirst propertyentailsthepresenceof thesecond,andviceversa.26 However,PPf is differentbecauseits presenceis entailedby theabsenceof anotherproperty. Joshihasthereforeintroducedacompletelynewdynamicinto Dowty’ssystem,changingit considerably.
Shethengivestwo syntacticfeaturerulesfor Marathi:24Therestrictedobject,OBJ�, which hasthe features[+r, +o], is omitted. Joshiarguesthat Marathi is a symmetriclanguage(i.e. it doesnot
distinguishbeweenadirectandindirectobject).25FarrellAckermanandhis students(p.c.)havemadethis point to me,in aslightly differentmanner.26Of course,theargumentthat receivesa certainproto-entailmentcanbesuppressed.For example,a passiveof a causativewill havethecauser
suppressed,but theentailmentsstill hold of thebasepredicateto which thepassiveis related.
16
(36) a. anargumentwith a P-Patientproperty) [�r]
b. all otherarguments) [�o]
(Joshi,1993:113)
Shealsoproposesthefollowing functionmappingprinciple:
(37) a. (i) [+volitional]
ARG ) subject;otherwise:
(ii) ARG ) subject[�r]
b. Otherargumentsaremappedontothe lowestcompatiblefunctionon themarkednesshierarchySUBJ
> OBJ,OBL.27
(Joshi,1993:113)
Finally, sheassumesthefollowing well-formednesscondition:
(38) TheSubjectCondition:everypredicatemusthavea singletokenof thegrammaticalfunctionSUBJ.(Joshi,1993:114)
Thebasicideaof Joshi’s analysisis thatproto-roleinformationdeterminessyntacticfeaturesandthesein turn deter-minegrammaticalfunction. I will now give anexampleof how this worksin Joshi’s system.Theexampleis for thenon-volitionalverbaawad
n
e (Joshi,1993:117).
(39) Mappingof grammaticalfunctionsin theverbaawad
n
e (‘like’)
Undergoeschangeofstate(PPa)
Not E.T.B.Sentient(PPf)
Semantics
aawadd
n
e h ARG ARG i ARG STR
‘to like’ [�r] [�r] . . . (36a)
S/O S/O . . . (37aii),(37b)
S O . . .SubjectConditionor O S . . .SubjectCondition
Dueto thesyntacticfeaturerulesin (36a),thetwo argumentsgetthefeatureassignment[�r], sincetheyhaveproto-patientproperties.Giventhis featureassignment,eitherargumentcanbea subject,accordingto (37aii). Finally, eachargumentcanequallybeanobject,accordingto (37b),sincethelowestrole in thegrammaticalfunctionhierarchythattheyarecompatiblewith is OBJ (obliquesare[+r]). Thus,shesuccessfullyaccountsfor thesubject/objectalternationusingthesemechanisms.Theotheralternatingpredicatesarehandledin a similarmanner.
Thisexamplealsoillustrateshowtheproblematicproto-patientproperty, PPf,is necessaryfor Joshi’sanalysis.It isclearthatthesecondargumentof aawad
n
e (‘like’) hasnoproto-patientpropertiesotherthanPPf. It doesnotundergoa changeof stateor location(PPa). It is not an incrementaltheme(PPb). It is not causallyaffectedby the otherargument(PPc).It is notnecessarilystationaryrelativeto theotherargument(PPd).Finally, it existsindependentlyof
27Thishierarchyarisesfrom theassumptionthat,in LMT, negativefeaturesarelessmarkedthanpositiveones(Joshi,1993:105).
17
theliking event(PPe).However,if thesecondargumentgetsnoproto-patientproperty, it doesnotgetthefeature[�r]by (36a),but rathergetsassigned[�o] by (36b). If it getsassigned[�o] thenit mustbethesubject,sinceobjectsare[+o]. But thentheoptionalityis unaccountedfor andtheanalysiswouldwronglypredictthattheverbaawad
n
e hasnosubject/objectalternationandthatthestimulus‘likee’ mustbethesubject(perhapsanalogouslyto Englishplease, asin Thefoodpleasedme). This is clearlynot thecase,accordingto theevidencethatJoshipresents.Thisverbexhibitsthesubject/objectalternationandthelikeecanbetheobjectaswell asthesubject.
Lookingat it anotherway, Joshi’s (1993)theoryyieldsthefollowing two generalizations:
(40) a. If a predicatehasa volitional argument,thatargumentis realizedasthesubject;otherwiseanyargu-mentof theverbthatpossessesa proto-patientpropertycanbethegrammaticalsubject.(Joshi,1993:92)
b. If thereis novolitional argumentandthereis morethanoneargumentthathasaproto-patientpropertytherewill besubject/objectalternation.
Both of theseprinciplesaresomewhatstrange. Although Joshiclearly demonstratesthe importanceof the proto-agentpropertyof volitionality, why shouldhavinga proto-patientpropertybe the second-mostimportantcriterionfor subjecthood?The whole point of Dowty’s (1991)theory is that proto-patiententailmentsarecharacteristicofobjects, notsubjects.Instead,(40)proposesthatthereis oneproto-agentpropertythatreallymattersfor subjecthood,andthatotherwisesubjectsareprincipally characterizedby proto-patientproperties.This is not at all in accordancewith (Dowty, 1991)and it is extremelyhard to seehow the different versionsof the theory(Dowty’s (1991)andJoshi’s (1993))couldpossiblybothbetrue.
Joshi’sanalysisis insightful in its useof proto-roleinformationandits avoidanceof anorderedargumentstructure.However,it is a parametricaccountandtheparticularinstantiationof theparametersshegivesarelargelyparochialto theanalysisof Marathi. Thus,heraccountassuchdoesnot makestrongcrosslinguisticpredictions.Furthermore,shestipulatesthevolitionality of thesubject,ratherthanderiving it from generalprinciples. Shealsoproposesthatproto-patientpropertiescharacterizesubjects.Lastly, her analysisrequiresthe useof the problematicproto-patientpropertyPPfto getthedescriptivefactsright.
In the following section,I providean OT accountof grammaticalfunction selectionin Marathi. This accountwill usea small setof general,universallyviable constraintsto derivethe correctlinking of argumentsin Marathi.In addition, it derivesthe volitionality of the subjectfrom the harmonicalignmentof two prominencescalesandsubsequentconstraintinteraction,andit doesnotuseproto-patientpropertyPPf.
6 An Optimality-theoretic Analysis
In this sectionI will provideanOT analysisfor thefollowing featuresof grammaticalfunctionselectionin Marathi:
(41) a. If apredicatehasavolitional argument,thatargumentis realizedasthesubject.
b. If thereis novolitional argumenttherewill besubject/objectalternation.
Theprincipaldifferencebetweenthis formulationandtheonein (40) is thatproto-patientpropertiesarenot assumedto becharacteristicof subjects.Rather,thealternationfactswill beshownto arisefrom a finer-grainedanalysisthatusesasmallsetof crosslinguisticallyviableconstraints.
6.1 StochasticOptimality Theory and Harmonic Alignment
Aissen(1999)hasmotivatedtheuseof prominencescalesandharmonicalignmentto capturemarkednessuniversals.I will adopther scaleSubj(ect)> Nonsubj(ect),where“>” means“more prominentthan”. I alsoproposethe newscaleProto-Agent> Proto-Patient(P-A > P-P).“Proto-Agent”and“Proto-Patient”shouldbe understoodascovertermsfor thesetof propertieslisted in section5. Thus,this scaleis really a partialordering,with theP-A properties
18
unorderedrelativeto eachother,andlikewisefor theP-Pproperties.Theideais that investigatingvariouslanguageswill provideevidencefor furtherorderingtheseproperties.In fact,theimportanceof volitionality in Marathiindicatesthatthispropertyis possiblythehighestrankedof theProto-Agentproperties.28 Thefully articulatedscaleI will adoptis P-Avol > P-A�vol > P-P.29
Now, I needto expressthenotionthatsubjectstendto bearproto-agentproperties,andnonsubjectstendto bearproto-patientproperties.This will becapturedusingtheprominencescalesfor proto-rolepropertiesandgrammaticalfunctions.Thereis a formalmethodin OT for aligningtwo scales(PrinceandSmolensky,1993;Aissen,1999),calledharmonicalignment.
(42) Alignment: Givena binarydimensionD1 with a scaleX > Y on its elementsX,Y, andanotherdimensionD2 withaascalea> b ... > z on its elements,theharmonicalignmentof D1 andD2 is thepairof Harmonyscales:
a. Hx: X/a� X/b � . . .� X/z
b. Hy: Y/z � . . .� Y/b � Y/a
Theconstraintalignmentis thepairof constrainthierarchies:
a. Cx: *X/z � . . .� *X/b � *X/a
b. Cy: *Y/a � *Y/b � . . .� *Y/z
(PrinceandSmolensky,1993:136)
The result of the harmonicalignmentis that it is more harmonicfor X’s to be a’s and for Y’s to be z’s. This iscapturedin theconstraintalignmentby having*X/a and*Y/z bethelowestrankedconstraintsin their hierarchies.Itis importantto realizethatthesehierarchiesarefixeduniversally. Forexample,no languagemayparochiallyhavetheranking*X/b � *X/z. On theotherhandthe resultinghierarchiesmaybe interspersed,so long asthe orderingforparticularhierarchiesis respected.
Thealignmentof thetwo scalesthatI amusingwill serveasa moreconcreteexample:
(43) Subj> NonsubjP-Avol > P-A�vol > P-P
a. Harmonicalignment:Subj/P-Avol � Subj/P-A�vol � Subj/P-PNonsubj/P-P� Nonsubj/P-A�vol � Nonsubj/P-Avol
b. Constrainthierarchies:*Subj/P-P� *Subj/P-A�vol � *Subj/P-Avol
*Nonsubj/P-Avol � *Nonsubj/P-A�vol � *Nonsubj/P-P
The resultingconstrainthierarchiesaresuchthat it is worst for a subjectto haveproto-patientpropertiesandfor anonsubjectto havethe proto-agentpropertyof volitionality. This doesnot meanthat objectscannotbe volitional;it just meansthat the leastpreferredsituationis onein which a predicateentailsthe propertyof volitionality for anargumentthatgetsmappedto anonsubjectgrammaticalfunction.30
With thesehierarchiesin place,I cannowpresenttheconstraintsetthatI will beusingin thisanalysis:28Davis(1996)postulatesthattheproto-agentpropertyof causation(PPc)is alsoamoreimportantfactor thantheotherproto-properties.I leave
this asidehere.29I havenotedthespecificvolitionality propertyby subscriptingP-A with vol . Similarly, I usethelabelP-A
�vol asa shorthandfor all theP-Apropertiesexceptvolitionality.
30In fact, this is avery importantdistinctionthatDowty (1991)discussesaswell. Theentiretheoryof proto-rolesis aboutwhatthemeaningof apredicateentailsfor thatpredicate’sarguments.Incidentalpropertiesof theactualfillers of theseargumentslotsarenotunderdiscussionhere.
19
(44) Constraints
a. MAX (proto)Proto-roleinformationin theinput is realizedin theoutput.
b. DEP(proto)Proto-roleinformationin theoutputis presentin theinput.31
c. THEMATICsubj
Everyclausehasa thematicsubject.32
d. *Nonsubj/P-AvolAvoid nonsubjectswith theproto-agentpropertyof volitionality.33
e. *Subj/P-PAvoid subjectswith proto-patientproperties.
f. *Subj/P-A�vol
Avoid subjectswith proto-agentpropertiesotherthanvolitionality.
g. *Nonsubj/P-A�vol
Avoid nonsubjectswith proto-agentpropertiesotherthanvolitionality.
h. *Nonsubj/P-PAvoid nonsubjectswith proto-patientproperties.
Thenon-stochasticrankingof theseconstraintsis:34
(45) Marathi rankingTHEMATICsubj � MAX (proto)� *Nonsubj/P-Avol� DEP(proto)�f*Subj/P-P, *Subj/P-A�vol, *Nonsubj/P-A�vol, *Nonsubj/P-Pg
In orderto provethata rankingis not justsufficient,butalsonecessary, asetof rankingargumentsmustbepresented.As notedin PrinceandSmolensky(1993:106–107),thisconstitutes“a potentialempiricalargumentthat[Constraint1]dominates[Constraint2].”
Thefollowing tableau(PrinceandSmolensky,1993:106,(160))will beusefulin explainingtheform of arankingargument.
(46) Ranking ArgumentC1 C2
hinputi
a. ☞ opt *b. subopt * !
31I alsoassumethat thereis amarkednessconstraint,* STRUC(proto),sincefaithfulnessconstraintsusuallyhaverelatedmarkednessconstraints.However,this constraintplaysno role in theanalysispresentedhere.
32 This constraintis relatedto the constraintSUBJ, which requiresthat every clausehasa subject,thematicor otherwise(SUBJ is thereforeanalogousto the ExtendedProjectionPrincipleof GovernmentandBinding Theoryor the SubjectConditionof LFG’s Lexical MappingTheory,a versionof which is given in (38) above). If a languagehasSUBJandeither DEP(proto) or * STRUC(proto) — the markednessconstraintappro-priatefor proto-roleproperties— outrankingTHEMATICsubj , it will realizeclausalsubjectslinked to argumentswithout proto-rolepropertiesasexpletives.However,if THEMATICsubj outranksSUBJ, therewill benoexpletivesubjects.Eithersubjectswith noproto-rolepropertieswill leadtoungrammaticality,or anotherargumentwill beco-optedassubject.Thelattercaseoccursin Marathiverbsthatexhibit thesubject/objectalternation.Marathidoesnot haveexpletivesubjects(Pandharipande,1997:133),soI will assumethat THEMATICsubj outranksSUBJandI do not includethelatterconstraintin my analysis.
33Lødrup(1999)usessimilar constraintsto THEMATICsubj and*Nonsubj/P-Avol in his OT analysisof linking in Norwegianpresentationalfocusconstructions.However,heusesthematicroles,suchasThemeandAgent, ratherthanproto-roles.His paperalsocontainsmoreextensivediscussionof theinteractionbetweenSUBJandTHEMATICsubj (seefootnote32).
34I presenttheactualstochasticversionin (48) below.
20
In this tableau,opt is acandidatethatis attestedin thedataset,andis thereforeknownto beawinningcandidate.Thehigherrankingconstraintmustnot haveanyviolationsfor opt, whereasthe lower rankingconstraintmusthaveoneor moreviolations. Theothercandidate,subopt, competeswith opt. Thehigherrankingconstraintmustbeviolatedby this candidate,whereasthelower rankingconstraintshouldnot beviolated.Sinceit is knownthatopt mustbeanoutput,thisconfigurationshowsthatC1 mustoutrankC2. If thereverseranking,C2 � C1, werepicked,optwouldbeerroneouslyrejectedin favourof subopt. Therefore,rankingargumentsareveryimportantin establishingtheempiricaladequacyof a givenrankingandto showthata givenrankingis bothnecessaryandsufficientfor thedatasetunderconsideration.AppendixC containsa full setof argumentsfor therankingin (45).
I am usingBoersma’s probabilisticallyrankedconstraints(seesection3.4.3)to captureoptionality, so the con-straintsin ranking (45) must be augmentedwith rankingvalues. Thereare four constraintsthat will capturethesubject/objectoptionality in Marathi: *Subj/P-P,*Subj/P-A�vol, *Nonsubj/P-A�vol, and*Nonsubj/P-P. SinceI donot haveinformationaboutwhich argumentmapsto which grammaticalfunction morefrequently, I will makethebaselineassumptionthat thereis true optionality. In otherwords, I will assigntheseconstraintsthe samerankingvalue,so that therankingdifferenceis 0. This yields4! (= 24) differentoptionalrankings;however,dueto theuni-versalhierarchiesin (43b),only six of theseoutcomesarepossiblewithoutviolatingeitherthehierarchy*Subj/P-P�*Subj/P-A�vol or *Nonsubj/P-A�vol � *Nonsubj/P-P. Thesix constraintorderingsare:
(47) a. *Subj/P-P� *Subj/P-A�vol � *Nonsubj/P-A�vol � *Nonsubj/P-P
b. *Subj/P-P� *Nonsubj/P-A�vol � *Subj/P-A�vol � *Nonsubj/P-P
c. *Subj/P-P� *Nonsubj/P-A�vol � *Nonsubj/P-P� *Subj/P-A�vol
d. *Nonsubj/P-A�vol � *Subj/P-P� *Subj/P-A�vol � *Nonsubj/P-P
e. *Nonsubj/P-A�vol � *Subj/P-P� *Nonsubj/P-P� *Subj/P-A�vol
f. *Nonsubj/P-A�vol � *Nonsubj/P-P� *Subj/P-P� *Subj/P-A�vol
Giventherankingvaluedifferenceof 0, eachof theserankingshasa 1
6probabilityof occurring.
A noteof explanationis in orderaboutthenatureof hierarchiesandtheir relationshipto thestochasticmodelofOT that I am assuminghere. By definition,hierarchiesreducethe numberof factorial rerankingsto just thosethatrespectthehierarchies,asdemonstratedin (47). Therearetwo waysto think aboutthis,bothof which areequivalentfor my purposes.We could think of thehierarchiesasa constrainton thegenerationof constraintrankings.On thisview, the hierarchy-violatingrankingswould neverbe generated.Or, we couldallow the all factorial rerankingstobegeneratedandthenfilter out anythatviolatethehierarchies.With respectto acquisition,this meansthatwhenthelanguagelearneris rerankingherconstraintsin responseto languagedata,in orderto respectthehierarchiestheillicitrankingsmusteithernot begeneratedor theremustbea filtering of rankingsthatviolatethehierarchiesbeforeeachevaluationstepin learning.35
Thispropertyof hierarchies,thattheyareeffectivelymeta-constraintsonrankingsof constraints,is a fundamentaloneandmustbe acknowledgedby anyOT analysisthat usesthem,no matterwhethertheanalysisusesregularOTor stochasticOT. Thus, hierarchiesadd a mechanismto OT, but this is not particularto stochasticOT. Althoughwe mustbe cautiousin expandingour theories,the descriptive,explanatory, and typologically predictivevalueofhierarchiesareclear,asmostnotablydemonstratedby Aissen(1999).However,theuseof hierarchiesandthefilteringout of hierachy-violatingconstraintrankings— in the mannerdiscussedhere— hasnot beencountenancedin theOT learnability literature. Their effect on learnabilityproofshasyet to be testedthoroughly. In the stochasticOTmodelI amassuming,thereis at presentnoway to implementthefiltering effectI havejust outlined.Theassumption
35IdaToivonen(p.c.)haspointedout thatthehierarchiesarenotnecessarilypartof thegrammar.Theymaybefunctionally/cognitivelymotivatedconstraints,Thentheydonotneedto beexpressedgrammaticallyatall, butratherconstrainthespaceof possiblelanguages.In thatcase,nolanguagethatviolatedtheconstraintswould beinstantiated,simply becausesuchlanguagesarecognitively impossible.This is aninterestingalternativeandan importantarchitecturalissue,but in the presentanalysisthehierarchiesareto be thoughtof aspurely formal grammaticalconstraints,with nofunctional motivation. Thus,I am makingthe architecturalassumptionthat hierarchiesaresimply anotherformal aspectof optimality-theoreticgrammars.
21
I ammakingis thatalthoughall of thehierarchicallyrankedconstraintshavebeenassignedthesamerankingvalue,on anyactualevaluationonly oneof thehierarchy-respectingrankingsin (47) will be instantiated.This is preciselyequivalentto Boersma’s stochasticOT modelaugmentedwith filtering for hierarchies,andgivesa similar effect toAnttila’s (1997b)stratifiedpartialorderings,butwithout theproblemsdiscussedin section3.4.2.
With this in mind, (48) presentsthe constraintorderingagain,but marked(with subscripts)for rankingvalues.Thereis no evidencefor anyoptionalityin theotherconstraintsandtheyhavethereforebeenassignedvaluesthatarefar apart.Thiswill havetheresultthatanyorderingotherthantheonestatedis extremelyunlikely to occur.
(48) StochasticMarathi ranking100THEMATICsubj � 90MAX (proto)� 80*Nonsubj/P-Avol� 70DEP(proto)� 60*Subj/P-P� 60*Nonsubj/P-A�vol � 60*Nonsubj/P-P� 60*Subj/P-A�vol
Theprobabilityof anyof theconstraintsseparatedby arankingdifferenceof 10switchingatevaluationtimeis 0.0002,(assuminga noisefactorof 2).36 However,theprobabilityof anytwo of thefinal four constraintsswitchingis 0.5;so,anyof thesix rankingsin (47) is equallylikely to occur.
Now that theconstraintsarein place,thenextstepis to characterizetheform of the inputsandthecandidates.Ifollow Bresnan(to appear)andKuhn (2001)in assumingthe input to bea basicsemanticform, includingargumentstructureinformation.However,I alsoassumethatargumentstructureis aseparatelevelof representationwithin LFGgrammars,calleda-structure,andthatit is relatedto c-structureby the� projectionfunctionandto f-structureby the�projectionfunction(Butt etal.,1997).Sinceproto-propertiespertainto argumentstructure,theyarerepresentedat thea-structurelevelandarealsopartof theinput. Thus,theinputsI amassumingarepairsof f-structuresanda-structures.As anillustration,theinput for thesentencein (49)wouldbethepair in (50),wherethefirst memberis anf-structureandthesecondis ana-structure.
(49) tyaathat
mulaa-laaboy-D/A
tethat
aaiskrimice-cream
aawad
te.likes
Thatboylikesthat icecream.
(50)
*
26666666666666666664
PRED ‘aawad
n
e’
GF1
266664
PRED ‘mulaa’PERS 3NUM SG
GEND MASC
DEIXIS +
377775x
GF2
2664
PRED ‘aaiskrim’
PERS 3NUM SG
DEIXIS +
3775y
TNS PRESENT
37777777777777777775
,
2666666666664
REL aawad
n
e
ARG1
264PROTO-AGENT
nPAb, PAe
oPROTO-PATIENT
nPPa
o375x
ARG2
264PROTO-AGENT
nPAe
oPROTO-PATIENT
n o375y
3777777777775
+
I haveabbreviatedtheproto-rolepropertiesasPAa–e(i.e. proto-agentpropertiesa to e) andPPa–e,wheretheletterscorrespondto the propertieslisted in section5, (34) and(35), savefor the proto-patientpropertythat Joshiadded(PPf),which I rejected.
This kind of input is cumbersomein tableaux.SoI will insteaduseinputsthatencodethesameproto-roleinfor-mationin amoreconcisemanner:
(51) harg1[PAb,PAe,PPa]arg2[PAe]i36Recallfrom section3.4.3thatthereis a randomvariablewith aGaussiandistributionof valuesassociatedwith eachconstraintrankingandthat
this yieldsaslightly different rankingat eachevaluation.
22
This abbreviatedformat representsonly the part of the input that is relevantfor my analysis. The correspondenceshouldbeclearoninspection.AlthoughI amassuminganunorderedargumentstructure,I will numbertheargumentssothatwhichgrammaticalfunctionstheyhavemappedto will bemoreobvious.
TheinputsI usegenerallycontaintheproto-roleinformationattributedto themin Joshi(1993).However,shedoesnot attributethe full setof proto-propertiesto predicatesin all cases,whereasI haveendeavouredto includeall of apredicate’s arguments’proto-properties(exceptfor PPf). Thus,my inputsarein certainrespectsricher thanJoshi’s,but only includethe necessaryproto-roleentailmentsof the predicatesin question,asmotivatedin Dowty (1991).AppendixA givestheproto-propertiesfor eachof thealternatingverbslistedby Joshi(1993).
The candidatesand outputswill be triples of c-structures,f-structuresand a-structures.37 Evidently, this willconsistof fixing theunderspecifiedGF featureto a fully specifiedgrammaticalfunction,SUBJ andOBJ in particular.SinceI amusingthearchitectureproposedby Butt etal. (1997),thisessentiallyamountsto specifyingthe� projectionfunction,which is thelinking function.
Onceagain,though,theselargef-structureanda-structurepairsaretoocumbersomefor tableaux,soI will abbre-viatetherelevantpartsof thecandidateslike so:
(52)"
SUBJ [arg1[PAb, PAe, PPa]]OBJ [arg2[PAe]]
#
In this example,oneargumenthasbeenlinked to thesubjectfunction,while theotherhasbeenlinked to theobjectfunction.Thenecessaryconstraintsandrankingarein placenow, andI havecharacterizedtheform of theinputsandcandidates.
6.2 Optionality in Marathi Linking
First, I will show how theseconstraintscapturethe subject/objectalternationin non-volitional transitives. In thetableaux,I havesuppressedtheconstraintrankingvalues.Instead,I haverepresentedthefact that*Subj/P-P, *Subj/P-A�vol, *Nonsubj/P-A�vol, and*Nonsubj/P-Pcanalternatewith numeralsabovetheseconstraints.Thesenumeralsarethenusedto indicatewhich constraintrankingresultsin which optimalcandidate.For example,the notation(1� 2 � 3 � 4) next to the pointingfinger is shorthandfor (*Subj/P-P� *Subj/P-A�vol � *Nonsubj/P-A�vol �
*Nonsubj/P-P).Thefirst casetoconsideris thatof verbsin the‘like’ classandthefirst twoverbsin the‘find’ class(seeappendixA).
Theseverbsall haveoneargumentthat is entailedto besentient(PAb), existsindependentlyof theevent(PAe), andundergoesa changeof state(PPa).Thesecondargumentis entailedto existindependentlyof theevent(PAe) andhasnootherproto-properties.
37 Bresnan(to appear)andKuhn (2001)proposethat thecandidatesetin bothproductionandcomprehensiondirectedoptimizationis actuallya setof c-structure,f-structurepairs(i.e. fhx,yi j x is a c-structureandy is an f-structureg). Thusthe candidatesandoutputsassumedherearetriples,sincetheyalsocontaina-structures.However,we canignorethec-structuresin theproductiongrammar,sincethelinking optionality onlyhasto do with grammaticalfunction informationin thef-structure.In section6.4,which dealswith ambiguityin thecomprehensiongrammar,thec-structureswill bediscussedfurther.
23
(53) Non-volitional transitives 1 1 2 3 4‘like’ class‘find’ class:saapad
n
e (to find)‘find’ class:aad
hal
n
e(to comeacross)
TH
EM
AT
IC
subj
MA
X(proto)
*Nonsubj/P
-A
vol
DE
P(proto)
*Subj/P
-P
*Subj/P
-A
�
vol
*Nonsubj/P
-A
�
vol
*Nonsubj/P
-P
harg1[PAb,PAe,PPa]arg2[PAe]i
a."
SUBJ [arg1[]]OBJ [arg2[]]
#* ! ****
b."
SUBJ [arg1[]]OBJ [arg2[PAe]]
#* ! *** *
c."
SUBJ [arg1[PPa]]
OBJ [arg2[]]
#* !** *
d."
SUBJ [arg2[PAb,PAe,PPa,PPb]]OBJ [arg1[PAe]]
#* ! ** ** *
e. (3� 1� 2� 4)
(3� 1� 4� 2)
(3� 4� 1� 2)
☞"
SUBJ [arg1[PAb,PAe,PPa]]OBJ [arg2[PAe]]
#* ** *
f. (1� 2� 3� 4)
(1� 3� 2� 4)
(1� 3� 4� 2)
☞"
SUBJ [arg2[PAe]]OBJ [arg1[PAb,PAe,PPa]]
#* ** *
Dueto thehigh-rankingTHEMATICsubj constraint,candidates(a)and(b)areknockedoutimmediately, sincetheyhavesubjectswith no associatedproto-properties.This alsoresultsin severalMAX (proto)violations,asthesecandidatesdo not realizeproto-roleinformationfrom the input. Candidate(c) doeshavea subjectwith a proto-property, thusnotviolating THEMATICsubj , but it is eliminatedby MAX (proto). In fact,anycandidatethatdoesnot realizeall of theproto-roleinformationin theinputwill beeliminatedin thismanner. Therefore,in subsequenttableauxI donotshowsuchcandidates.Similarly, candidate(d) hasa fatal violation of DEP(proto),sincetheproto-propertyPPbis presenton its subject,but is notpresentin theinput. I will alsosuppressfurtherDEP violators.
This leavescandidates(e) and(f). In this tableauandanyothertableauxwith 1� 3, candidate(f) is thewinner.However,on anyof the threeotherlegitimaterankingsof theoptionalconstraints(where3 � 1), candidate(e) willbe the winner. Thus, theseconstraintsyield subject/objectalternationfor non-volitionaltransitives,sincethe onlydifferencebetweenthecandidatesis in whichargumentgetsmappedto whichgrammaticalfunction.38
38It shouldbe notedthat nothing in this analysisdistinguishesbetweenobjectsandobliques,which is anotherimportant facet of linking inMarathi, sinceit is a symmetriclanguage.I assumethatseparatecasedistributionprincipleswill makethenecessarydistinctions,asobliquesarealwaysmarkedwith obliquecaseor with apostposition(Pandharipande1997;Joshi1993;seesectionC, example(2c)).
In fact, assuggestedby JoanBresnan(p.c.), in orderto capturevariousgeneralizationsaboutsubjectsandobjectsrankinghigherthanobliquesaccordingto certaincriteria, it maybenecessaryto assumea prominencescalesuchasCore> Oblique,in additionto theSubject> Nonsubjectscaleassumedhere. We could align the proposednew scalewith the proto-role scaleas well, thus enablingus to makestatementsabouttherealizationsof thematicinformation in obliques. The resultingconstraints,alongwith the constraintsfor statingthe casefactsmentionedabove,couldthenbeusedin accountingfor theobjectsymmetryproperty,aswell aspossiblyrelatedfacts,suchasthecauseein anactivecausativebeingrealizedasanoblique.
A third alternativeis to incorporatethe OT constraintsproposedby Alsina (2000) for distinguishingfirst and secondobjectsin asymmetric
24
ThenextcaseI consideris thatof thefinal two ‘find’ classverbs,mil
n
e (to get)andlaabhn
e (to cometo possess).Theseverbssharethesameproto-propertiesastheother‘find’ verbs,but thesecondargumentis in additionentailedto undergoachangeof location(PPa).
(54) Non-volitional transitives 2 1 2 3 4‘find’ class:mil
n
e(to get)‘find’ class:laabhn
e (to cometo possess)
TH
EM
AT
IC
subj
MA
X(proto)
*Nonsubj/P
-Avol
DE
P(proto)
*Subj/P
-P
*Subj/P
-A
�
vol
*Nonsubj/P
-A
�
vol
*Nonsubj/P
-P
harg1[PAb,PAe,PPa]arg2[PAe, PPa]i
a. (1� 3� 2� 4)
(1� 3� 4� 2)
(3� 1� 2� 4)
(3� 1� 4� 2)
(3� 4� 1� 2)
☞"
SUBJ [arg1[PAb,PAe,PPa]]
OBJ [arg2[PAe,PPa]]
#* ** * *
b. (1� 2� 3� 4) ☞"
SUBJ [arg2[PAe,PPa]]OBJ [arg1[PAb,PAe,PPa]]
#* * ** *
The additionof a singleproto-propertyhasresultedin a drasticchangein distribution. Now the first candidateisselectedon five out of six rankings,while the secondcandidateis selectedonly on the ranking1 � 2 � 3 � 4.Foreshadowingthe discussionin section7 somewhat,thereis a differencebetweencandidates(a) and(b) in termsof proto-propertiesthatexplainswhy candidate(a) is favoured.Notethatarg2hasoneproto-agentpropertyandoneproto-patientproperty;so,it is just asgooda subjectasit is anobject.However,arg1hastwo proto-agentpropertiesandoneproto-patientproperties.This meansarg1is a bettersubjectthanobject. Candidate(a) hasarg1mappedto SUBJ andarg2mappedto OBJ, which is whatwe would expectaccordingto proto-roletheory, asarg1is a bettersubject,while arg2is asgoodanobjectasit is a subject.
Next I turn to the first setof passiveditransitives.Theseverbsall haveoneargument(arg2)that is entailedtobesentient(PAb), existsindependentlyof theevent(PAe), undergoesa changeof state(PPa),is causallyaffectedbythedemotedfirst argument(PPc),andis stationaryrelativeto themovementof the third argument(PPd).Theotherargument(arg3)is entailedto exist independentlyof theevent(PAe), movesrelativeto thesecondargument(PAd),undergoesa changeof location(PPa),andis causallyaffectedby thedemotedfirst argument(PPc).
languagesandfor distinguishingobjectsfrom obliquesin symmetricandasymmetriclanguages.
25
(55) Passivesof ditransitives 1 1 2 3 4den
e(to give)paat
hawn
e(to send)bharawn
e(to feed)wikn
e (to sell)
TH
EM
AT
IC
subj
MA
X(proto)
*Nonsubj/P
-Avol
DE
P(proto)
*Subj/P
-P
*Subj/P
-A
�
vol
*Nonsubj/P
-A
�
vol
*Nonsubj/P
-P
hØ arg2[PAb, PAe, PPa,PPc,PPd] arg3[PAd, PAe, PPa,PPc]i
a. (3� 4� 1� 2) ☞"
SUBJ [arg2[PAb, PAe, PPa,PPc,PPd]]OBJ [arg3[PAd, PAe, PPa,PPc]]
#*** ** ** **
b. (1� 2� 3� 4)
(1� 3� 2� 4)
(1� 3� 4� 2)
(3� 1� 2� 4)
(3� 1� 4� 2)
☞"
SUBJ [arg3[PAd, PAe, PPa,PPc]]OBJ [arg2[PAb, PAe, PPa,PPc,PPd]]
#** ** ** ***
Candidate(a) is selectedon only oneout of six constraintrankings,andcandidate(b) is selectedon the otherfiverankings.Again,thecandidatethatis favouredis theonewhosemappingsrespectthepredictionsof proto-roletheory.Arg3 hastwo proto-agentpropertiesandtwo proto-patientproperties,while arg2hasthreeproto-patientpropertiesandonly two proto-agentproperties.This meansthatarg2makesa betterobjectthansubject,while arg3is asgoodasubjectasit is anobject.Candidate(b) mapsarg2to OBJ andarg3to SUBJ,
Thesecondsetof passiveditransitives,presentedin tableau(56),consistsof thesemanticallysimilarverbslinhin
e(to write (aletter))andsaangn
e (to tell). Theseverbshaveoneargument(arg3)thatis causallyaffectedby thedemotedfirst argument,which is thewriter or teller (PPc),andthatdoesnotexistindependentlyof theevent(PPe),astheletteror statementcomesinto existencethroughbeingwritten or told. Theotherargument(arg2)is entailedto besentient(PAb), existsindependentlyof the event(PAe), undergoesa changeof state(PPa),and is causallyaffectedby thedemotedfirst argument(PPc).
26
(56) Passivesof ditransitives 2 1 2 3 4linhin
e (to write (a letter))saangn
e (to tell)
TH
EM
AT
IC
subj
MA
X(proto)
*Nonsubj/P
-A
vol
DE
P(proto)
*Subj/P
-P
*Subj/P
-A�
vol
*Nonsubj/P
-A
�
vol
*Nonsubj/P
-P
hØ arg2[PAb, PAe, PPa,PPc] arg3[PPc,PPe]i
a. (1� 3� 2� 4)
(1� 3� 4� 2)
(3� 1� 2� 4)
(3� 1� 4� 2)
(3� 4� 1� 2)
☞"
SUBJ [arg2[PAb, PAe, PPa,PPc]]OBJ [arg3[PPa,PPc]]
#** ** **
b. (1� 2� 3� 4) ☞"
SUBJ [arg3[PPa,PPc]]
OBJ [arg2[PAb, PAe, PPa,PPc]]
#** ** **
Unlike theotherpassiveditransitives,arg2is not stationaryrelativeto themovementof arg3. Therefore,arg2lacksthe proto-patientpropertyPPd. This meansthat arg2for thesepassiveditransitiveshastwo proto-agentpropertiesandtwo proto-patientproperties,meaningthereis nothingto distinguishit asanobjectversusa subject.On theotherhand,arg3onlyhasproto-patientproperties,makingit agoodobjectbutabadsubject.Weseeagainthatcandidate(a)which is preferredon five out of six rankings,is theonewhereproto-roletheoryis respectedandarg2is SUBJ whilearg3is OBJ.
This leavesusonly with thepassivesof ditransitivecausativesto consider. Theseareagainbrokeninto two sets,accordingto their proto-properties.Thefirst sethasoneargument(arg2)that is entailedto besentient(PAb), existsindependentlyof theevent(PAe), undergoesa changeof state(PPa),andis causallyaffectedby thedemotedcauser(PPc). The otherargument(arg3) is entailedto exist independentlyof the event(PAe), but bearsno otherproto-properties.
(57) Passivesof ditransitive causatives1 1 2 3 4aikawn
e (to hear.CAUS)hungawn
e (to smell.CAUS)sikawn
e (to teach)
TH
EM
AT
IC
subj
MA
X(proto)
*Nonsubj/P
-A
vol
DE
P(proto)
*Subj/P
-P
*Subj/P
-A
�
vol
*Nonsubj/P
-A
�
vol
*Nonsubj/P
-P
hØ arg2[PAb, PAe, PPa,PPc] arg3[PAe]i
a. (3� 1� 2� 4)
(3� 1� 4� 2)
(3� 4� 1� 2)
☞"
SUBJ [arg2[PAb, PAe, PPa,PPc]]OBJ [arg3[PAe]]
#** ** *
b. (1� 2� 3� 4)
(1� 3� 2� 4)
(1� 3� 4� 2)
☞"
SUBJ [[arg3[PAe]OBJ [arg2[PAb, PAe, PPa,PPc]]
#* ** **
27
Notice that the input in this tableauis similar to the onefor non-volitionaltransitives1 in (53), exceptthat the firstnon-suppressedargument(arg1in (53), arg2in (57)) hasonemoreproto-patientpropertyin (57). In bothcasesthefinal argumentmakesa bettersubjectthanobject,asits only proto-propertyis PAe. And the first (non-suppressed)argumentis thebestobject,asit is theonly argumentwith anyproto-patientproperties.But, thefirst argumentalsohasmoreproto-agentpropertiesthanthefinal argumentdoes.Sowe havea situationin which thesameargumentisboththebestsubjectandthebestobject.Sincethegrammarattemptto assignthesameargumentto bothsubjectandobject,eachoptimalcandidatewins in half thecases,with onewinningwhen3� 1 andtheotherwinningwhen1�3.
Lastly, let usconsiderthefinal passiveditransitivecausative,paa>dzn
e (to feed),whichhasslightly differentproto-propertiesfrom the causativesin (57). Arg2 hasthe sameentailments,but arg3hasadditionalentailmentsbeyondthat of existing independently(PAe). The other entailmentsaremovementrelative to the position of arg2 (PAd),undergoingchangeof location(PPa),beingtheincrementaltheme(PPb),andbeingcauallyaffectedby arg2.
(58) Passivesof ditransitive causatives2 1 2 3 4paa
>dzn
e (to feed)
TH
EM
AT
IC
subj
MA
X(proto)
*Nonsubj/P
-A
vol
DE
P(proto)
*Subj/P
-P
*Subj/P
-A
�
vol
*Nonsubj/P
-A
�
vol
*Nonsubj/P
-P
hØ arg2[PAb, PAe, PPa,PPc] arg3[PAd, PAe, PPa,PPb,PPc]i
a. (1� 2� 3� 4)
(1� 3� 2� 4)
(1� 3� 4� 2)
(3� 1� 2� 4)
(3� 1� 4� 2)
☞"
SUBJ [arg2[PAb, PAe, PPa,PPc]]OBJ [arg3[PAd, PAe, PPa,PPb,PPc]]
#** ** ** ***
b. (3� 4� 1� 2) ☞"
SUBJ [arg3[PAd, PAe, PPa,PPb,PPc]]
OBJ [arg2[PAb, PAe, PPa,PPc]]
#*** ** ** **
Thispresentsyetanothercaseof subject/objectalternationwith preferencefor onecandidateovertheother. Arg2 hasequallymanyproto-agentandproto-patientproperties,andthereforeservesequallywell assubjector object.However,arg3hasmoreproto-patientpropertiesthanproto-agentproperties,makingit a betterobjectthansubject.Onceagainthis is reflectedin thecompetition:candidate(a) is selectedon five out of six rankings,andthis is thecandidatewitharg2assubjectandarg3asobject.
6.3 Volitionality
Tableaux(53)to (58)showthattheOT grammaroutlinedheredoesindeedgetthesubject/objectalternationin all threerelevantcases.Thus,fact (41b)hasbeenaccountedfor: whenthereis no volitional argumentthereis subject/objectalternation.Theanalysisalsoaccountsfor fact (41a),whichstatesthatif thereis a volitional argument,it mustbethesubject.This is where*Nonsubj/P-Avol — thetop rankedconstrainton thenonsubjectgrammaticalfunction/proto-rolealignment— comesin.39
39In this tableau,I haveglossedover all of the proto-rolesexceptthat of volitionality, representingthemwith ellipses(. . . ). Similarly, I havesuppressedDEP(proto)andthevariablerankingconstraints,astheseplay no role in selectingthewinner.
28
(59) Volitional transitives
TH
EM
AT
IC
subj
MA
X(proto)
*Nonsubj/P
-A
vol
harg1[PAa, . . . ] arg2[.. . ]i
a."
SUBJ [arg2[.. . ]]
OBJ [arg1[.. . ]]
#* !
b."
SUBJ [arg2[.. . ]]OBJ [arg1[PAa, . . . ]]
#* !
c. ☞"
SUBJ [arg1[PAa, . . . ]]OBJ [arg2[.. . ]]
#
Candidate(a) showsthat the propertyof volitionality, if presentin the input, mustbe realizedin the output,or elseoffendingcandidatesareeliminatedby MAX (proto).Candidates(b) and(c) showthatoncethepropertyof volitionalityis realized,it mustberealizedonthesubject,asrealizingit on theobjectresultsin aviolationof *Nonsubj/P-Avol. Incaseswheretheinput hastwo argumentsmarkedasvolitional, suchasin theverb‘khel
n
e’ (to play with), theobjectwill bemarkedfor volitionality, eventhoughthisviolates*Nonsubj/P-Avol, becausenot realizingthepropertywouldresultin aviolationof MAX (proto),which is worse(seetableau(2a)in appendixC).
Thus,this analysiscorrectlyaccountsfor the fact thatsubjectsin Marathiarevolitional, andthat in caseswherethereis no volitional argument,therewill bea choicein linking argumentsto grammaticalfunctions. In section7, Icomparethis approachto Joshi’s. But first, let usconsiderhow thisgrammardealswith ambiguityin thecomprehen-siondirection.
6.4 Ambiguity
In section2, I arguedthatoptionalityandambiguityin OT canbeviewedasoppositesidesof thesamecoin. If weview thedifferenceasonebetweenproductionandcomprehensiondirectedoptimization,thentheinputsof eachkindof optimizationcorrespondto theoutputsof theother(Smolensky,1996c,1998).Thus,bothoptionalityandambiguitycouldbeviewedasoneinputto theoptimizationhavingmultipleoutputs,with thenatureof inputsandoutputsvaryingin theappropriateway.
Recallfrom section2 thatoneway of handlingineffability in OT is throughtheuseof bidirectionaloptimization.If weassumeSmolensky’s(1998)formulationof bidirectionaloptimization,thentheinputsto productionaresemanticforms(like the f-structure/a-structurepair in (50)) andthe inputsto comprehensionarestrings. Theconstraintsandtheir rankingareidenticalin bothproductionandcomprehension,though.
Fromthestring inputsto thecomprehensiondirectedoptimization,thefunctionGen will yield thecandidatesetof (semantic)f-structures.However,we wantto makesurethatonly thosef-structuresthatmatchtheinput stringaregenerated.As mentionedbriefly in section6 (seefootnote37),astandardassumptionin OT-LFG is thatthecandidatesetin bothproductionandcomprehensiondirectedoptimizationis a setof c-structureandf-structurepairs(Bresnan,to appear;Kuhn,2001)andI haveextendedthis to includea-structures.In comprehension,we needto considerthestring yield of the c-structuremembersof thesetriples. Only thosetriples whosec-structureyieldsmatchthe inputstringaregenerated(Kuhn,2001).
As motivatedin Kuhn (2001)andJohnson(1998),considerationof the string’s yield resultsin an asymmetrybetweengenerationandparsingwhichmeansfurtherstepshaveto betakenfor parsing.In generation,aninputto Gengivescandidateswith all kinds of (ungrammatical)stringsasthe yields of their c-structuremembers.Kuhn (2001)
29
givestheexampleof theinput for a wh-questionhavingasoneof thegeneratedstringsShedo readwhat. Now, if wewereto try andparsethis string,everycandidatein thecandidatesetwill havethis stringasits yield. So no matterwhichcandidateis optimal,thecandidateshouldactuallybeungrammaticalandshouldnotbegenerated.
Kuhn (2001)thereforeproposesa filtering functionfor theresultingoptimalcandidatesof comprehensionto getbackthekind of input f-structuresthatareusedin generation(i.e. semanticforms).Thenabackwardsgenerationstepis executedandonly theoptimalcandidate(s)with astringyield thatmatchestheyield of thestringbeingparsedarese-lected.If nosuchcandidateexists(asin theexample),theinputstringto comprehensionis rejectedasungrammatical.This is essentiallybidirectionaloptimization.
6.4.1 Ambiguity in Marathi Linking
TheMarathidatathat I havebeenexaminingdoespresenta caseof ambiguity: thestringsthatdisplayoptionalityinlinking (i.e. non-volitionaltransitivesandpassiveditransitives)areambiguousregardingwhich nounphrasein thestringis assignedto thesubjectroleandwhich is assignedto theobjectrole. Thiscanbeviewedasakind of structuralor parsingambiguity.40
Let us now observehow the constraintsystemdevelopedfor non-volitionaltransitivesin productionworks incomprehension.
40At first this may not seemlike a true semanticambiguity, sincethe meaningis constant. But, oncewe embedtheseambiguoussentencesin broadercontextswhich showdiscourseeffects,we shouldbe able to observethe semanticeffectsof this ambiguity. In particular, SUBJ is adiscoursefunction, whereasOBJ is not. Therefore,in context,the stringsunderconsiderationherewill be ambiguous,aseitherNP could be thesubjectdiscoursefunction.
30
(60) Non-volitional transitives(comprehension)
1 2 3 4
TH
EM
AT
IC
subj
MA
X(proto)
*Nonsubj/P
-A
vol
DE
P(proto)
*Subj/P
-P
*Subj/P
-A
�
vol
*Nonsubj/P
-A
�
vol
*Nonsubj/P
-P
“tyaa mulaa-laate aaiskrimaawad
te.” 41
a. (3� 1� 2� 4)
(3� 1� 4� 2)
(3� 4� 1� 2)
☞
*
26666666666666666664
PRED ‘aawad
n
e’
SUBJ
266664
PRED ‘mulaa’PERS 3NUM SG
GEND MASC
DEIXIS +
377775x
OBJ
2664
PRED ‘aaiskrim’PERS 3
NUM SG
DEIXIS +
3775y
TNS PRESENT
37777777777777777775
,
2666666666664
REL aawad
n
e
ARG1
264P-A
nPAb, PAe
oP-P
nPPa
o375x
ARG2
264P-A
nPAe
oP-P
n o375y
3777777777775
+
* ** *
b. (1� 2� 3� 4)
(1� 3� 2� 4)
(1� 3� 4� 2)
☞
*
26666666666666666664
PRED ‘aawad
n
e’
SUBJ
2664
PRED ‘aaiskrim’
PERS 3NUM SG
DEIXIS +
3775y
OBJ
266664
PRED ‘mulaa’
PERS 3NUM SG
GEND MASC
DEIXIS +
377775x
TNS PRESENT
37777777777777777775
,
2666666666664
REL aawad
n
e
ARG1
264P-A
nPAb, PAe
oP-P
nPPa
o375x
ARG2
264P-A
nPAe
oP-P
n o375y
3777777777775
+
* ** *
I haveonly presentedthetwo winningcandidates(dependingon therankingof thefloatingconstraints,asbefore).Any candidatethat doesnot havea GF SUBJ will be knockedout by THEMATICsubj . The interpretationof the
faithfulnessconstraints,MAX (proto) and DEP(proto), is morecomplex,but unproblematic.Proto-roleinformationessentiallyconcernsentailmentsfrom predicatemeanings,in this caseverb meanings.In lexical look-up, the verbpatlaabringsalongwith it theseentailmentsandhencetheproto-roleinformation.In otherwords,to understandthestring,a hearerwould haveto necessarilyunderstandthewordsin thestring. Sincetheproto-roleinformationis partof themeaningof patlaa, or at leastdirectly inferablefrom thatmeaning,thesefaithfulnessconstraintscanoperatebycomparingthe inferableproto-roleinformationto the informationpresentin thecandidates.Theywill thusservetoruleoutcandidateswith thewrongproto-properties(i.e. thewronginferences).
Therefore,eithercandidate(a) or (b) will win, dependingon how thefloating constraintsarefixed at evaluationtime. Applying Kuhn’sfiltering function,wegetthefollowing semanticform:42
41This is example(49) from section6.1: tyaathat
mulaa-laaboy-D/A
tethat
aaiskrimice-cream
aawad
te.likes
Thatboylikesthat ice cream.42Technically,this is therecoveredsemanticform for candidate(a). Candidate(b) would haveGF1 andGF2 swapped.However,thereis no real
distinctionhereasthefeaturesareidentical.Theindexis thereonly to respectfunctionaluniqueness.
31
(61)
*
26666666666666666664
PRED ‘aawad
n
e’
GF1
266664
PRED ‘mulaa’PERS 3NUM SG
GEND MASC
DEIXIS +
377775x
GF2
2664
PRED ‘aaiskrim’
PERS 3NUM SG
DEIXIS +
3775y
TNS PRESENT
37777777777777777775
,
2666666666664
REL aawad
n
e
ARG1
264PROTO-AGENT
nPAb, PAe
oPROTO-PATIENT
nPPa
o375x
ARG2
264PROTO-AGENT
nPAe
oPROTO-PATIENT
n o375y
3777777777775
+
The grammaticalfunctionsSUBJ and OBJ havebeenunderspecifiedto GF1 and GF2. By the notationalconventiondiscussedonpage22,this is equivalentto:
(62) harg1[PAb,PAe,PPa]arg2[PAe]i
Thereadercanseeby referringto tableau(53) thatthis is indeedtheinput for non-volitionaltransitivesin theproduc-tion grammar. Similar resultscanbeshownfor passiveditransitivesandpassiveditransitivecausatives.I will not gothroughtheseotherresultshere.
Thus,theOTgrammarfragmentpresentedherecanaccountfor boththesyntacticproductionoptionalityin Marathilinking and the comprehensionambiguity for the sameconstructions.This showsthat productionoptionality andcomprehensionambiguityareformally thesamein OT grammars.In otherwords,thesystemof continuously-rankedconstraintsthatwasusedto captureoptionalitycanalsobeusedto captureambiguity. In this paper,I haveillustratedthisby lookingat thesamedomain.However,I seenoprincipledreasonwhy othertypesof ambiguitythatdonothaveoptionalityin theproductiondirectioncouldnotbehandledin thesamemanner.
7 Comparing the OT Approach with Joshi’sApproach
Joshi’s analysisnot only accountsfor the subject/objectalternation,it also accountsfor the fact that Marathi is asymmetriclanguage,andfor severalintriguingfactsaboutlogicalsubjectsandlogicalobjects.Theanalysisdevelopedhereonly accountsfor thealternationfacts. With respectto just thesefacts,therearefive majordifferencesbetweenmy analysisandJoshi’sandI will arguethattheseprovidereasonsfor preferringthisanalysis.
Onemajor differenceis that the OT analysisusesa small numberof crosslinguisticallyviable constraintswithanorderingspecificto Marathi,whereasJoshi’s syntacticfeaturerules(in (36)) andfunctionmappingprinciples(in(37)) arepart of a parametricapproachto languagevariation,with the relevantparameterssetfor Marathi. It mayseemthat the two approachesaremoreor lessequivalent,but while it is not obviousfrom Joshi’s expositionwhatthe relevantdimensionsof variationbetweenlanguagesmight be, the OT approachmakesspecificclaims(throughconstraintreordering)aboutlanguagevariation. Of course,this is not a specificproblemwith Joshi’s analysis,butratheraninstanceof theproblemof relatingparametersacrosslanguagesanddecidingwhatthelimits of thespaceofparametricvariationare.
The seconddifferenceis that in the OT analysisthe alternationbetweensubjectandobjectlinking andthe factthat volitional argumentsarerealizedassubjectsareboth derivedfrom the alignmentof the grammaticalfunctionandproto-roleprominencescales.This alignmentyieldedthe automaticconsequencethat volitional argumentsaresubjects,without everstipulatingit directly. However,Joshi’s LFG accountdoesstipulatethis propertydirectly (in(37a)).
Third, Joshi’s analysisusesproto-patientpropertiesascriteriaof subjecthood.Recallthegeneralizationsin (40)from section5:
32
(40) a. If a predicatehasa volitional argument,thatargumentis realizedasthesubject;otherwiseanyargu-mentof theverbthatpossessesa proto-patientpropertycanbethegrammaticalsubject.(Joshi,1993:92)
b. If thereis novolitional argumentandthereis morethanoneargumentthathasaproto-patientpropertytherewill besubject/objectalternation.
Oncethe proto-agentpropertyof volitionality doesnot apply, subjectchoiceis entirely dependenton proto-patientproperties. But proto-patientpropertieswere proposedfor object selection,not subjectselection(Dowty, 1991).Joshi’s proposalthereforerunscontraryto thetheorythatshepresupposes.TheOT analysisrejectstheproposalthatproto-patientpropertiesdeterminesubjectchoiceandthusmaintainsproto-roletheory’soriginal insights.
Thefourthdifferenceis thatJoshiproposesanewproto-patientproperty, “not entailedto besentient”(PPf),whichI do not adopt. As discussedin section5, therearetwo problemswith this proto-property. First, it would fit betterwith proto-roletheoryif this propertywas“entailedto benon-sentient”,which is a strongerentailmentandin directoppositionto theproto-agentpropertyof sentience(PAb). Many of theproto-agentandproto-patientpropertiesarein directoppositionto eachother(Dowty, 1991),in particularPAc-d andPPc-d,andthis wouldbeanothersuchcase.This bringsus to the secondproblem: any argumentof a verb that lacks the proto-agentpropertyof sentiencewillautomaticallyhavethe propertyof not beingentailedto besentient.Theproto-patientpropertythat Joshiproposesis directly entailedby this absenceandis superfluous,becauseits presenceis completelypredictable.Note that thiscontrastswith PAc-d andPPc-d,becausethesearesuchthat if, for example,oneargumenthastheentailmentof therelevantPA property, thena differentargumentgetstheopposingentailment.Thus,thenewpropertydoesnot fit intoproto-roletheoryandits motivationis suspect.
The fifth differencebetweenmy analysisandJoshi’s alsohasto do with Dowty’s proto-roletheoryandhasin-terestingimplicationsfor that theory. Originally, the ideabehindDowty’s analysiswasthat theargumentwith moreproto-agentpropertiesgetslinked to the subjectfunction,whereasthe argumentwith moreproto-patientpropertiesgetslinked to theobjectfunction.Any kind of alternationin linking would bepredictedto only occurif thereis a tieon the numberof relevantproperties.But, in bothJoshi’s analysisandthis one,this is no longerstrictly true. If itweretrue,only thedative-markedargumentin non-volitionaltransitives(e.g.the‘finder’ in the‘find’ class),couldberealizedasthesubject,sincethis argumenthastwo proto-agentproperties,whereastheotherargumentonly hasone(seetableau(54)).
In Joshi’sanalysis,this featureof Dowty’s theoryis simplygivenup. However,in theanalysispresentedhereit isstill preserved,but only asa tendency, ratherthanasanabsoluteproperty. Lookingbackat tableau(54) for the‘find’classof non-volitionaltransitives,onealternateis pickedfive out of six times,whereastheotheroneis pickedonlyonein six times.43 The striking fact is that the alternatethat is pickedmostoften is the onewherethe subjecthasmoreproto-agentpropertiesthantheobject.44 Similarly, theopposingtendencyof objectsto havemoreproto-patientpropertiesis illustratedin tableau(55). In that tableau,onecandidateis againrealizedfive timesout of six, whereastheotheris realizedonly onceout of everysix times.Thecandidatethat is pickedmostoftenis theonein which theobject,ratherthanthesubject,hasthemostproto-patientproperties.45 Thus,theargumentwith themostproto-agentpropertiesis preferentiallybut not obligatorily linked to the subject,andthe argumentwith the mostproto-patientpropertiesis againonly preferentiallylinked to theobject.
Anotherconsequenceof thisanalysisis thattheoptionalityin linking is predictedto disappearif thereis anasym-metrybetweenargumentson both the numberof proto-agentpropertiesandthe numberof proto-patientproperties.Thatis, if thefirst argumenthasmoreproto-agentpropertiesthanthesecondandthesecondargumenthasmoreproto-patientproperties,thenthefirst argumentmustbelinkedto thesubjectandthesecondargumentmustbelinkedto the
43How we interpretthesepredictedfrequenciesis an openquestion. It seemsunrealisticto interpretthemasraw frequencies.I believetheyshouldinsteadbeinterpretedasencodingthestrengthof thetendencyin question.
44Thetendencyis purelydueto thedifferencein thenumberof proto-agentproperties,sinceeachargumentin thewinning candidatesin (54)hasexactlyoneproto-patientproperty.
45In the sameway that proto-patientpropertiesarekept constantacrossthe two argumentsin (54), eachargumentof the winning candidatesin (55) hastwo proto-agentproperty. The tendencyin argumentrealizationnotedin this caseis thereforeentirely dueto the differing numberofproto-patientproperties.
33
object,aswould be thecasein Dowty’s original theory. This canbe demonstratedeasilyby usingsimilar inputstotheonesthatyieldedoptionality. I will showthataddinga proto-patientpropertyto thesecondargumentin the‘find’classof non-volitionaltransitivewould removetheoptionality.
(63) 1 2 3 4
TH
EM
AT
IC
subj
MA
X(proto)
*Nonsubj/P
-A
vol
DE
P(proto)
*Subj/P
-P
*Subj/P
-A
�
vol
*Nonsubj/P
-A�
vol
*Nonsubj/P
-P
harg1[PAb,PAe,PPa]arg2[PAe,PPa,PPb]i
a."
SUBJ [arg2[PAe,PPa,PPb]]OBJ [arg1[PAb,PAe,PPa]]
#** * ** *
b. ☞"
SUBJ [arg1[PAb,PAe,PPa]]OBJ [arg2[PAe,PPa,PPb]]
#* ** * **
Accordingto the hierarchiesin (43b),*Subj/P-Pmustoutrank*Subj/P-A�vol and*Nonsubj/P-A�vol mustoutrank*Nonsubj/P-P. Therefore,either*Subj/P-Por *Nonsubj/P-A�vol mustbethehighestrankingconstraintat evaluationtime. Whenever*Subj/P-Pis rankedhighest,candidate(b) wins. This is thecandidatewhich hastheargumentwithmostproto-patientpropertieslinked to theobjectandtheonewith mostproto-agentpropertieslinked to thesubject.And thiscandidatealsowinswhenever*Nonsubj/P-A�vol is rankedhighest,becauseit is alsothecandidatein whichthe object hasthe leastproto-agentproperties.46 Therefore,there is no optionality: the argumentwith the mostproto-agentpropertiesis linked to thesubjectandtheargumentwith themostproto-patientpropertiesis linked to theobject.47
8 Conclusion
In this paper,I haveshownhow continuously-rankedconstraintsin a stochasticmodelof OT canbeusedto describeoptionality and ambiguity in syntax. I arguedthat this methodwas preferableto other approachesto optionalityproposedin theOT literature.Finally, I appliedthemethodto describinga specificcaseof syntacticoptionalityandambiguity:argumentlinking in certainMarathipredicates.
Theanalysisdevelopedherebuildson thatof Joshi(1993)in usingproto-roleinformation.However,while Joshihasto giveupcertainfactsaboutproto-rolesandgrammaticalfunctionselectionoriginally proposedby Dowty (1991),thisanalysispreservesthoseinsightsasstatisticaltendencies.Furthermore,thisanalysisusescrosslinguisticallyviableconstraintsandonly a simplemechanismof constraintaligment.Thus,theOT analysisof subject/objectalternationin Marathihasthreeattractivefeatures.First, it usesa smallnumberof crosslinguisticallyviableconstraints.Second,onemechanism— harmonicalignment— is usedto accountfor boththesubject/objectalternationandthefact thatvolitional argumentsmustbesubjects.Third, andmostinterestingly, this analysispreservesasa tendencyDowty’sobservationthat theargumentwith mostproto-agentpropertiesis realizedasthesubjectandthat theargumentwiththemostproto-patientpropertiesis realizedastheobject.
I haveshownthat it is possibleto representlinking using a small set of general,violable constraints. I haveillustratedhow optionality canbe handledin OT syntaxby using the linking theorydevelopedhereto providean
46It is easyto observein thetableauwhy this shouldbetrue.Sincethetwo candidateshaveexactlythesameconstraintviolation profile for both*Subj/P-Pand*Nonsubj/P-A
�vol, it doesnotmatterwhich of theseconstraintsis rankedhighest.47Thesameeffectcanbeshownusingthewinning candidatesin tableau(55),by addingaproto-agentproperty(otherthanvolitionality) to arg2.
34
accountof thesubject/objectalternationin Marathi. I havealsoshownhow ambiguityin comprehensiongrammarscanbe modelledusingthe samemethods.This givessomeindicationthat Optimality Theorycanin fact dealwithineffability, optionalityandambiguityandis aviablesyntactictheory.
35
Appendices
A Verb Classes48
Verbs Proto-Properties
Non-volitional transitives (‘like’ class) ARG1 ARG2aawad
n
e to like [PAb, PAe, PPa] [PAe]sama
>dzn
e to realize [PAb, PAe, PPa] [(PAe)]umagn
e to understand [PAb, PAe, PPa] [PAe]disn
e to notice [PAb, PAe, PPa] [PAe]pat
n
e to accept(mentally) [PAb, PAe, PPa] [PAe]ru>tsn
e to agree(aesthetically) [PAb, PAe, PPa] [PAe]maanawn
e to suit (mentally, medically) [PAb, PAe, (PPa)] [PAe]kal
n
e to hear/learn [PAb, PAe, PPa] [PAe]
Non-volitional transitives (‘find’ class) ARG1 ARG2saapad
n
e to find [PAb, PAe, PPa] [PAe]aad
hal
n
e to comeacross [PAb, PAe, PPa] [PAe]mil
n
e to get [PAb, PAe, PPa] [PAe, PPa]laabhn
e to cometo possess [PAb, PAe, PPa] [PAe, PPa]
Passiveditransitives ARG2 ARG3den
e to give [PAb, PAe, PPa,PPc,PPd] [PAd, PAe, PPa,PPc]paat
hawn
e to send [PAb, PAe, PPa,PPc,PPd] [PAd, PAe, PPa,PPc]bharawn
e to feed [PAb, PAe, PPa,PPc,PPd] [PAd, PAe, PPa,PPc]wikn
e to sell [PAb, PAe, PPa,PPc,PPd] [PAd, PAe, PPa,PPc]linhin
e to write (letter) [PAb, PAe, PPa,PPc] [PPa,PPc]saangn
e to tell [PAb, PAe, PPa,PPc] [PPa,PPc]
Passiveditransitive causatives49(ingestives) ARG2 ARG3aikawn
e (aikn
e) to hear.CAUS (to hear) [PAb, PAe, PPa,PPc] [PAe]hungawn
e (hungn
e) to smell.CAUS (to smell) [PAb, PAe, PPa,PPc] [PAe]sikawn
e(sikn
e) to teach(to learn) [PAb, PAe, PPa,PPc] [PAe]paa
>dzn
e(pin
e) to feed(to drink) [PAb, PAe, PPa,PPc] [PAd, PAe, PPa,PPb,PPc]
B Marathi GlossesD/A = dative/accusativecase50 ERG = ergativecase GEN = genitivecaseABIL = ability case PART = participle CAUS = causativeABLE = ability verbalmorpheme(Joshi,1993:viii)48The first two columnsof this table are takenfrom Joshi(1993: 52–60)and arenot meantto be exhaustive.The last two columnsare the
proto-propertiesI haveassumed.49Thebasicverbfrom which thecausativeis formedis givenin parentheses.50SeeJoshi(1993:26–30)for argumentsfor collapsingthesetwo casesinto one.
36
C Ranking Arguments
(1) a. THEMATICsubj � MAX (proto) TH
EM
AT
IC
subj
MA
X(proto)
harg1[PAa,PAe,PPa,PPf]i
a.h
OBJ [arg1[PAa,PAe,PPa,PPf]]i
* !
b. ☞h
SUBJ [arg1[PAa,PAe,PPa]]i
*
b. output= ‘gelaa’ (went)
c. raamRam
pail-tiraa-laaother-bank-LOC
gelaa.went
Ramwentto theotherbank(of river).(Joshi,1993:24,(30a))
(2) a. MAX (proto)� *Nonsubj/P-Avol MA
X(proto)
*Nonsubj/P
-A
vol
harg1[PAa,PAb,PAe] arg2[PAa,PAb,PAe]i
a."
SUBJ [arg1[PAa,PAb,PAe]]
OBL [arg2[PAb,PAe]]
#* !
b. ☞"
SUBJ [arg1[PAa,PAb,PAe]]OBL [arg2[PAa,PAb,PAe]]
#*
b. output= ‘khel
n
e’51 (to playwith)
c. sudhaSudha.3sf
anusı/barobarAnu.inst.3sf/with
khel
teplay.pres.3sf
Sudhaplayswith Anu.52
(Pandharipande,1997:295,(847))
51Also bhan
d
n
e (to fight with), andboln
e (to talk with) (Pandharipande,1997:295).52Thisexampleis from Pandharipande(1997)anduseshertranscriptionandglossstyles.
37
(3) a. *Nonsubj/P-Avol � DEP(proto) MA
X(proto)
*Nonsubj/P
-A
vol
DE
P(proto)
harg1[PAb,PAe,PPa]arg2[PAa,PAe]i
a."
SUBJ [arg1[PAb,PAe,PPa]]OBJ [arg2[PAa]]
#* * !
b. ☞"
SUBJ [arg1[PAb,PAe,PPa]]OBJ [arg2[PAe,PPf]]
#* *
b. output= ‘aawad
n
e’ (to like)
c. tyaathat
mulaa-laaboy-D/A
tethat
aaiskrimice-cream
aawad
te.likes
Thatboylikesthat ice cream.(Joshi,1993:55,(42a))
(4) a. DEP(proto)� *Subj/P-P MA
X(proto)
DE
P(proto)
*Subj/P
-P
harg1[PAb,PAe,PPa]arg2[PAe,PPa,PPf]i
a."
SUBJ [arg1[PAb,PAe]]OBJ [arg2[PAe,PPa,PPb,PPf]]
#* * !
b. ☞"
SUBJ [arg1[PAb,PAe,PPa]]OBJ [arg2[PAe,PPf]]
#* *
b. output= ‘aawad
n
e’ (to like)
(5) a. DEP(proto)� *Subj/P-A�vol M
AX
(proto)
DE
P(proto)
*Subj/P
-P
*Subj/P
-A
�
vol
harg1[PAb,PAe,PPa]arg2[PAe,PPa,PPb,PPf]i
a."
SUBJ [arg1[PPa]]OBJ [arg2[PAe,PPa,PPb,PPc,PPf]]
#** * ! *
b. ☞"
SUBJ [arg1[PAb,PAe,PPa]]OBJ [arg2[PAe,PPf]]
#** * **
b. output= ‘aawad
n
e’ (to like)
38
(6) a. DEP(proto)� *Nonsubj/P-A�vol M
AX
(proto)
DE
P(proto)
*Nonsubj/P
-A
�
vol
harg1[PAb,PAe,PPa,PPb]arg2[PAe,PPf]i
a."
SUBJ [arg1[PAb,PAe,PPa,PPb,PPc]]OBJ [arg2[PPf]]
#* * !
b. ☞"
SUBJ [arg1[PAb,PAe,PPa]]OBJ [arg2[PAe,PPf]]
#* *
b. output= ‘aawad
n
e’ (to like)
(7) a. DEP(proto)� *Nonsubj/P-P MA
X(proto)
DE
P(proto)
*Nonsubj/P
-A
�
vol
*Nonsubj/P
-P
harg1[PAa,PAb,PAd,PAe] arg2[PPf]i
a."
SUBJ [arg1[PAa,PAb,PAd,PAe,PPa]]OBJ [arg2[]]
#* * !
b. ☞"
SUBJ [arg1[PAa,PAb,PAe]]OBJ [arg2[PPf]]
#* *
b. output= ‘saat
hawn
e’ (to store/collect)
c. tyaathat
mulaa-niboy-ERG
kaahisome
naan
icoins
saat
hawli.stored
Thatboystored(collected)somecoins.(Joshi,1993:195,(51a))
D Citation Information for Marathi Examples
Joshi (1993) Joshi (1993) Joshi (1993)Example Page Example Example Page Example Example Page Example(19a) 53 (36a) (19b) 53 (37a) (20) 60 (51b)(21) 60 (53b) (22a) 38 (2a) (22b) 38 (2b)(23a) 39 (4a) (23b) 39 (4b) (24a) 39 (5a)(24b) 39 (5b) (25a) 54 (38b) (25b) 54 (39)(26a) 54 (40b) (26b) 55 (41b) (27a) 61 (54b)(27b) 61 (55) (28a) 62 (56b) (28b) 62 (56c)(29a) 59 (48b) (29b) 58 (48a) (30a) 59 (49b)(30b) 59 (49a) (31a) 64 (61a) (31b) 65 (61b)(32a) 65 (62a) (32b) 65 (62b) (49) 55 (42a)
39
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