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A beginners guide to Optimality Theory

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1. A beginner's guide to Optimality Theory. Moira Yip. UCL. 2. Part I. The Basics. 3. The philosophy ... Phonologists have always said things like The glottal ... – PowerPoint PPT presentation

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Title: A beginners guide to Optimality Theory


1
A beginners guide to Optimality Theory
  • Moira Yip
  • UCL

2
Part I
  • The Basics

3
The philosophy
  • Q Why does phonology exist?
  • A To improve the form of phonological outputs
  • Phonologists have always said things like The
    glottal stop is inserted because the syllable
    needs an onset
  • OT builds this directly into the grammar.

4
The pioneers
5
Paul Smolensky
Alan Prince, n.d.
Alan Prince
6
Structure of an OT Grammar
  • Output-based theory, for a given input all
    possible outputs are compared against a set of
    constraints
  • One-step, non-derivational
  • Universal set of constraints
  • Two types Markedness constraints, that penalize
    disfavored outputs, e.g. NoCoda
  • Faithfulness constraints, that penalize changes
    to input e.g. NoDeletion

7
How do languages differ?
  • Every language uses the same set, but prioritizes
    them differently
  • Constraints may compete. For a CVCCV input,
    compare two syllabifications
  • CV.CCV violates ComplexOnset
  • but CVC.CV violates NoCoda
  • Neither output is perfect
  • Conflicts resolved by ranking constraints.
    English ranks NoCoda gtgt Complex Onset (re.ply),
    but Cantonese reverses this (yap.lej)

8
Why arent all syllables ta?
  • Faithfulness constraints stop wholesale return to
    an unmarked state.
  • Main trio
  • NoDeletion (Max)
  • NoInsertion (Dep)
  • NoChange (Ident)

9
Example
  • Suppose input is /kæt/
  • Output kæt violates NoCoda
  • kæ violates NoDeletion
  • kæ.ti violates NoInsertion
  • If NoDeletion, NoInsertion gtgt NoCoda kæt will
    win.

10
Extensions of Faithfulness
  • Faithfulness relates input to output, but
    extended to
  • Base-reduplicant relations
  • Paradigms and sets of related words
  • Loanword to source language

11
How it works
  • Maori
  • Active Passive Gerund
  • -ia -a?a
  • hopu hopukia hopuka?a to catch
  • aru arumia aruma?a to follow
  • NoCoda gtgtNoDeletion

12
A tableau
13
German final devoicing
  • lant landen to land
  • tak tage days

14
Factorial typology INoCoda, Onset, Del, Ins
  • Del, Insert gtgt NoCoda, Onset
  • Onsets opt., and codas permitted. English
  • NoCoda, Onset gtgt Del, Insert
  • No codas, and onset obl. Hua, Siona
  • NoCoda gtgt Del, Insert gtgt Onset
  • No codas, onsets opt. Hawaiian
  • Onset gtgt Del, Insert gtgt NoCoda
  • Onsets obl, codas permitted Cantonese

15
Factorial typology cont
  • Del gtgt Onset/NoCoda gtgt Insert
  • Repairs by epenthesis Arabic
  • Insert gtgt Onset/NoCoda gtgt Del
  • Repairs by deletion Maori

16
Factorial typology cont..
  • For n constraints, n! grammars, where n!
    n(n-1)(n-2).
  • But some constraints may not interact, so their
    relative ranking may have no effects, and there
    will not be n! languages.
  • This is true for Onset and NoCoda above

17
Submerged constraints The Loch Ness monster
phenomenon
  • English violates NoCoda and Onset freely (eat)
  • Yet they govern syllabification of intervocalic
    C
  • guitar g?.ta, not g?t.a
  • Conclusion Constraints may be below the
    surface, but are not turned off
  • Distinguishes OT from a parameter approach

18
Thanks to Mary Pearce
19
The Emergence of The Unmarked (TETU)
  • A language may violate constraints in most of the
    grammar, but observe them in certain
    circumstances
  • Reduplication only CV syllables may surface,
    even if language allows CCV or CVCC elsewhere
  • Tagalog ta-trabaho
  • Ponapean ke-kens
  • Faith-IO gtgt Markedness gtgt Faith-BR

20
Part II Issues and results
  • (phonology and morphology only!)

21
Are all rankings possible grammars?
  • Factorial typology says Yes.
  • However, some are thought to be fixed in UG,
    usually because they are rooted in phonetics.
  • E.g. All languages prefer vocalic nuclei, but
    some allow sonorants (English little l?.tl),
    a very few allow stops (Berber tf.tkt)
  • One-way entailment If stops then sonorants, but
    not vice-versa
  • Nucleus/stop gtgt Nucleus/Sonorant gtgt
    Nucleus/vowel

22
How can we limit the constraint set?
  • All should be plausibly universal
  • The resulting factorial typology should not
    predict unlikely grammars
  • Introduction of a new constraint should be a last
    resort, only when an effect cannot be shown to
    result from the interaction of existing
    constraints.

23
Are all constraints universal and innate?
  • Maybe. Some proposals instantiate morphemes as
    constraints, and these must be language-specific
    and learnt.
  • E.g. English Plurals, Possessives. For
    possessive plurals, one s satisfies both
    constraints, so there is no need to add two
    cats , catss
  • There may be schemas, from which the child
    builds a constraint. E.g. Align L/R (X,Y) where
    X, Y are morphological or prosodic categories.

24
The opacity problem
  • In phonology, opacity refers to cases where the
    context for a process is not surface apparent (or
    not surface-true).
  • Tiberian Hebrew /de?/ becomes dee, as a
    result of epenthesis into a final cluster (cf
    /melk/ gt melex) and deletion of final glottal
    stop (cf. /qara?/ gt qara). Ordered rules handle
    this easily. /de?/ ?dee? ? dee
  • BUT In non-derivational OT, the easiest way to
    satisfy CC and ? is to choose de.

25
Why de can never win
26
Solutions? Not yet, but watch this space
  • Deny the phenomenon (Mielke and Hume, Green)
  • Sympathy theory (McCarthy)
  • Constraint conjunction (Kirchner, Ito and Mester)
  • Output-output constraints (Steriade, Burzio)
  • Stratal OT (Kiparsky)
  • Comparative markedness (McCarthy)
  • Candidate chains (McCarthy)

27
John McCarthySomewhere in between these
pictures he did some OT..
2001
1971
28
What is an output?
  • How much phonetic detail do outputs contain?
    Maybe a great deal.
  • Work of Steriade and her students shows
    faithfulness to fine phonetic details of
    duration, etc, and role of perceptual cues in the
    grammar.

29
Language variation
  • Some models allow free ranking, so that two
    candidates tie, and suggest that this accounts
    for 50/50 free variation.(Antilla)
  • Others propose that constraints have a range of
    ranking positions, and that one can read off this
    the actual frequencies of different variants
    (Stochastic OT Hayes, Boersma)

30
Constraint A
Constraint B
Constraint A gtgt Constraint B most of the time,
but occasionally B gtgt A, for example if we
select the rankings at the arrows
31
First-language acquisition
  • Many data-based studies, including Gnanadesikan
    1995, Hayes 1999, Boersma 2001, Dinnsen and
    Gierut 2001, Goad 2001, Stemberger and Bernhardt
    2002, Pater and Wehrle 2001,
  • Also much work on computational issues and
    learning algorithms, especially Tesar and
    Smolensky, Prince, Samek-Lodovici, Pulleyblank
    and Turkel, and others

32
Initial state
  • Language acquisition must involve re-ranking, as
    the grammar evolves
  • Likely starting point Markedness gtgt
    Faithfulness. Early utterances unmarked. Adult
    dog is child d?, with NoCodagtgtNoDeletion.
  • Re-ranking happens when child notices NoCoda
    violations in adult, and demotes NoCoda below
    NoDeletion.
  • Gradual Learning Algorithm (GLA) (Boersma and
    Hayes)

33
Stages in acquisition
  • Each stage should involve a demotion of a
    markedness constraint on the basis of positive
    evidence
  • But stages are not abrupt and discrete
    stochastic OT.
  • GLA makes only tiny adjustments, so constraints
    slide past each other, allowing for a period of
    variation.

34
What can go wrong?
  • Deviant constraint set?
  • Deviant initial ranking?
  • Deficit in learning algorithm?
  • A few studies, such as Specific Language
    Impairment (SLI), phonological disorders,
    Prader-Willi Syndrome (Gamble, MFM 2005)

35
SLI Marshall 2005
  • Some SLI children (selected for morphosyntactic
    deficits) also have phonological disorder.
  • Past tense affixation may be omitted if it would
    create a voiced cluster rob, not robbed. English
    observes this constraint in monomorphemic words.
  • VoicedClustergtgt Realize-affix
  • Constraint set seems to be normal.
  • They seem to have defective re-ranking
    algorithms unmarked utterances persist after
    usual age.

36
Common vs rare phonological disorders (Kinney
2004)
  • Common disorders Markedness gtgt Faithfulness
    Fric gtgt Nochange Stopping
  • Less common Unusual rankings of faithfulness
    constraints. NoDel gtgt NoCoalescence, so /sp/ gt
    f
  • Rarest Reversal of usual markedness
    hierarchies s, h, t gtgt f?, so /s/ gt f?

37
Types of disorder
  • M gtgt F normal, but delayed. Learning algorithm
    at fault
  • F 2gtgtF1 possibly skewed initial state
  • M2 gtgtM1 Oddly structured constraint set

38
Stuttering
  • Diment, Howell, and Harris 2005, and many other
    studies from Pete Howells lab.
  • Not OT, but nonetheless show a clear role of
    phonological markedness in increased stuttering
    rates
  • More likely in forms which violate
  • NoCoda
  • Fricative
  • ComplexOnset
  • Both sCC clusters
  • BUT Less likely in syllables which violate
  • Onset

39
The assessment and treatment of phonological
disorders (Barlow 2001)
  • Generally, markedness gtgt faithfulness, producing
    stopping (shoe gt tu), cluster reduction, coda
    deletion (sled gt l?), gliding (rain gt
    wen)
  • Specific treatment goals target constraints that
    need to be demoted. Most marked aspects targeted
    first, producing greatest change. For example, if
    final f,l,r are targeted, all of Fric, liquid,
    NoCoda will be demoted at once.
  • Effects thus widespread If tell and Tess can
    be correctly produced, sled will be too.

40
Summary
  • OT developed for adult language competence,
  • Implies a theory of acquisition, including
    possible pathways
  • Suggests ways of thinking about the locus of
    disorders
  • May predict that certain interventions may be
    most effective

41
Todays programme
  • Alan Prince, Rutgers
  • Hubert Truckenbrodt, Tübingen
  • Vieri Samek-Lodovici, UCL
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