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MPhil Seminar: Evaluating OT L1/L2 Acquisition and learnability 8-3-2007.

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1 MPhil Seminar: Evaluating OT L1/L2 Acquisition and learnability 8-3-2007

2 Overview  Focus on comparison of leading theories (RBP : OT) What each predicts to be possible and impossible How these predictions compare to the data  General acquisition effects  SLA effects

3 Scientific study of acquisition: research questions theory data Top-down: what does each theory predict to be possible and impossible? Bottom-up: what are the central acquisition phenomena that any theory must account for?

4 Evaluating theories  Grammar/Theory 1 Lexicon: {a} Rules:  a optionally  b  Grammar/Theory 2 Lexicon: {a} Rules:  a optionally  b  b optionally  c Venn diagram of outputs for grammar 1 and grammar 2 a b c grammar 2 grammar 1 Loci of predictive difference box = range of conceivable outputs for any theory Is a theory that makes fewer predictions better?

5 Some basic learning issues  All existing OT learning models (except some flawed nascent work by Tesar) require being handed correct URs, SRs, and CON.  How can an OT grammar be learned, given the large number of constraint rankings that can produce the limited data set to which a language learner is exposed? Does UG provide a default ranking of constraints?  Prince and Smolensky 203: since constraints can’t be inferred from surface data (because they’re violable), they must be part of UG  Sherrard 1997 and McMahon 2000: in OT 10 (untied) constraints produce 36 million possible grammars, posing a learning problem, whereas rules simply state observable generalisations.  McMahon and Calabrese: the first step in learning the grammar is observing alternations/generalizations. DP stops there, but OT requires an extra step of going from there to a constraint ranking. Why is this necessary?

6 Typical OT presentation of the learning task  Prince and Smolensky 1993: All constraints are universal, occurring in all languages.  Languages only vary with respect to their lexicons (input forms) and the rankings of the constraints.  Implication: If constraints are innate, you don't have to learn them.  Constraints that have to be learned anyway morpheme constraints  Leftmost(um): Place the morpheme –um- in the leftmost position possible. (P&S 1993).  clitic alignment constraints, à la Anderson 1994. parameterisable constraints  RhythmicType=Iamb/Trochee: Feet are iambic/trochaic. (P&S 1993)  conjoined constraints?  Any two simple constraints can be conjoined to form a complex constraint. Are all possible conjunctions of simple constraints universal? Can you conjoin a previously conjoined constraint?

7 Hale and Reiss 1998  Fact: child language production differs from comprehension Children understand [k h æt] but produce [ta] Smolensky 1996: Optimality Theory (OT) can generate this Hale and Reiss: no it can’t, and it shouldn’t (1) The nature of child phonology a. The Strong Identity Hypothesis, which holds that child phonology is governed by the same principles as adult phonology [H/R] b. The view that child phonology is fundamentally distinct from adult phonology— that it licenses processes unattested in adult language, that it depends on a series of developmental stages, and so on [S] (2) The nature of the evidence a. Deviations from target forms—in children’s as well as adults’ grammars—are to be attributed to performance effects, including nonlinguistic cognitive and motor processing. [H/R] b. Many deviations from target forms are the result of ‘‘child phonology’’ (i.e., the child’s phonological competence)—grammatical effects for which the target language provides no evidence. [S]

8 OT learning algorithms M >> F GLA CDA

9 Smolensky’s scheme  initial state of grammar: M >> F  production: OT grammar selects most harmonic output for a given input  comprehension: OT grammar selects most harmonic input for an observed output  correction algorithm: when the comprehension evaluation is less harmonic (i.e. there are more asterisks) than the production evaluation, relevant constraints are demoted to make the comprehension parse more harmonic than the production parse

10 Smolensky’s scheme

11 Problems with S identified by H/R  can’t account for surface neutralization, e.g. German /rad/, /rat/  [rat] Asudeh 2001:7 “OT grammars seem to be non-trivially non-reversible, whether they use interpretive parsing or not. This is in conflict with the reversibility prized by other constraint-based architectures and is tantamount to having separate grammars for production and comprehension, which is conceptually undesirable.”  it isn’t possible to have a steady-state grammar of Smolensky’s type, because the correction algorithm will always kick in immediately and rerank the constraints  can’t account for Captain Hazelwood’s drunken speech (misarticulation of liquids, final devoicing, deaffrication), which in OT requires reranking of constraints (see next slide on performance)  S has severe problems with chain shifts and opacity effects.  S can’t account for higher accuracy in direct imitation: i.improved performance under concentration  S predicts simply better pronunciation of [s] for [š] ii. parroting  S would have to require instantaneous ranking of Cree constraints!

12 competence vs. performance  Johnson, Pisoni, and Bernacki (1990) report on the intoxicated speech of the captain of the Exxon Valdez around the time of the accident at Prince William Sound, Alaska. (669)  Observed effects: misarticulation of /r/ and /l/ final devoicing Deaffrication  Performance problem, not different grammar  Cf. mimicking Cree sentences doesn’t mean that one has acquired Cree grammar (671)  attempts at producing adult [p h εn] pen collected from a 15-month-old child in a 30-minute period (Ferguson 1986): [mã ə ], [ v  ], [dε dn ], [ hIn ], [ m bõ], [ pHIn ], [ tHn  tHn  tHn  ], [ba h ], [ dhau  ], [buã]  Kids who can’t speak yet but can accurately identify subject and object of sentence, distinguish phonemes, etc. (Hirsh-Pasek and Golinkoff 1991)Hirsh-Pasek and Golinkoff 1991

13 Hale and Reiss’s C-P model

14 Some problems with OT acquisition theories identified by Menn 2000  mushy and non-phonemicized early outputs  rules that are no longer constraint-based  U-shaped developmental curves  the need to differentiate ‘input’ in the sense of ‘the adult form the child is trying to approximate’ from ‘input’ in the sense of ‘abstract strings of concatenated morphemes to be parsed’

15 The Gradual Learning Algorithm  Boersma and Hayes 2001  “The GLA is cast within a stochastic version of OT that places constraints on a numeric scale (Boersma 1998).  Each time the grammar is used to evaluate Input-Output pairings, the ranking values are perturbed slightly (by ‘evaluation noise’) before converting the values to a ranking order. This produces variation between the rankings of constraints whose values are close to one another.  In the version proposed by Boersma and Hayes (2001), the values of all constraints preferring the winner are raised, and the values of those preferring the loser are lowered, all by an equal amount termed the plasticity.  “The result is that the model is much closer to a standard stochastic grammar than would at first appear; phonological templates which are abundantly instantiated in the training set end up being highly favored by the grammar, and those which are poorly instantiated end up disfavored. Thus, the rankings of constraints closely track the frequency values that would be assigned to the same constraints in some stochastic grammars.” (Pierrehumbert 2003)

16 Problems: GLA  non-convergence (Pater 2005)—see later slide  Pierrehumbert 2003:225-6 requires constraints like *ks to get morphophonemic rules like velar softening  in fact, one needs to postulate a universal set of constraints pairing each phoneme with each other phoneme (i.e. *ks, *kt, *kp…)  “the price of folding unnatural morphophonological correspondences into the phonological grammar is splitting the correspondences into unrelated constraint pairs, which are ranked separately.” “In the GLA model, all constraints are at the same level and all constraint rankings are trained on the same data set. Thus, the connection drawn above between the granularity of constraints and the size of the effective training set does not appear to be available.” “Phonetics, in common with many other physical processes, provides examples of skewed [e.g. gamma] distributions relating to physical nonlinearities and saturations…In contrast, distributions arising from repeated independent decisions (as in coin-flipping, or a forced choice experiment) tend to be Gaussian. Since the assumption of Gaussian distributions is critical to the mathematical tractability of [GLA], the existence of non-Gaussian distributions appears to be problematic.” “The GLA model also does not distinguish effects relating to type frequency from effects relating to surface, or token, frequency.” “It also provides no way to downweight the grammatical impact of extremely frequent words, as Bybee (2001) and Bailey and Hahn (2001) show to be necessary.”

17 The Constraint Demotion Algorithm  Tesar and Smolensky 1993 version, as reported by Crosswhite and Keer

18 Problems: CDA  learning of unnatural patterns  can’t deal with variation (Tesar and Smolensky 1998, Boersma and Hayes 2001) see later slide

19 Learning unnatural patterns  CDA predicts that natural patterns will be easier to learn than unnatural ones, because they require fewer departures from UG ranking. Support from Pater’s 2005 study?  On the other hand, if learning involves formulating rules and their difficulty is computed on the basis of their formal structure, then unnatural patterns should be equally learnable. Supported in studies by Pycha et al. 2003, Morrison 2005, and Seidl & Buckley (forthcoming)

20 Variation is a problem for constraint demotion algorithms  “inconsistent data, such as variation in the ambient language, causes RCD to choke” McCarthy 2002:204-5 Cf. Hayes 2000 Intra-individual variability in L2 production  Tropf 1987:174—multiple renditions of German nicht ‘not’ in a one-hour session with a Spanish speaker learning German: nIS 31, nI 25, nISt 4, nIs 4, nIk 4, nEt 2, nIC 2, nI  t 1, nIZ 1  Similar findings in L1 acq Hayes’ alternative (strictness bands) predicts that only adjacent constraints can be involved in optionality  Problem: Pierrehumbert’s hovacity data

21 Non-convergence for GLA in complex credit scenarios (Pater 2005)  “The GLA will succeed on the learning problem presented in the last section if it is run in “demotion only” mode, in which it only decreases the values of loser preferring constraints, and does not change the value of winner preferring constraints. However, as Boersma (1997, 1998) shows, this version of the GLA fails to converge in cases involving variation.”

22 Variation in CDA  from Pater 2005

23 Second Language Acquisition

24 OT on SLA  “We assume that second language acquisition involves creation of a new grammar, using the same resources as first language acquisition (though other cognitive strategies may be used as well). One major difference, however, is that the initial state of second language acquisition is the final state of first language acquisition” (Pater and Tessier 2005)

25  M/F-based learning No opacity, derived environment, or avoidance effects that don’t appear in L1 or L2 wouldn’t make sense to spontaneously invoke constraint conjunction, Null Parse, sympathy constraints, etc.  Consistency Consistent cross-linguistic treatment of a given phenomenon, e.g. resolution of theta same constraints as characterize natural languages, so *D/_# will always be dealt with via devoicing, etc.  No levels no level-based effects, since there are no levels unnatural processes will not be imported into L2, because they are morphologically conditioned (according to Lombardi, Steriade, etc.)  Markedness IL effects will result from either hidden UG rankings in L1, or from intermediate degrees of constraint demotion/promotion; NOT from reversion to UG rankings when already superseded by L1 rankings markedness-based changes will conform to universal markedness hierarchy neutralizations will be in direction of unmarked member of opposition Natural/unmarked patterns will be easier to learn Predictions of canonical OT

26  Contra M/F-based learning: Nonderived environment blocking Opacity Avoidance  Contra Consistency: Optionality and variation Convention vs. automaticity Final devoicing  Contra No levels: Level-based interference Unnatural interference  Contra Markedness: Cases where IL phenomenon  NL, TL Unnatural patterns not harder to learn Some central SLP phenomena

27 Nonderived Environment Blocking  Eckman and Iverson 1995 et seqq.: Suppression of s-palatalization in Korean acq of English Suppression of spirantization in Spanish acq of English  Kiparsky and Menn 1987:47—derived environment effect in acq of Greek  Polish devoicing and raising with loanwords snop (not *snup) but pagoda → pagut, toga → tuk  Standard OT treatment of NDEB: constraint conjunction (Łubowicz 1999) Smolensky: only postulate CC as warranted by PLD  DEC is problem for: OT claim that grammars only differ in constraint ranking OT’s rejection of generalizations/rules—DEC in SLA is clearly a generalization kicking in, not a constraint conjunction spontaneously appearing

28 Opacity in SLP  Counterfeeding chain shift substitution Cho and Lee 2001, Idsardi 2002 on opacity in Korean acq of English  sin → s j in + thin → sin same is found in L1 (Dinnsen, O’Connor, and Gierut 2001) opaque substitution = contrast maintenance + ordering, not sympathy, turbidity, targeted constraints, etc.  Smolensky: only postulate constraint conjunction as warranted by PLD Idsardi 2002: “this spontaneous chain shift…does not reflect properties of their original L1 grammar, the target L2 grammar, or of Universal Grammar…only by employing persistent rules can we correctly create the conditions for chain-shift; persistence of constraints and constraint rankings into the L2 does not correctly induce the chain-shifting behavior”  Counterbleeding repairs Weinberger 1987:412—Mandarin learners of English who apply final epenthesis before final C-cluster simplification, e.g.  [aen ә ]  Counterfeeding and counterbleeding in toy L2 acq already discussed earlier in course

29 Avoidance  speakers sometimes avoid complex L2 configurations even if their L1 has them Laufer and Eliasson 37, Jordens 1977, Kellerman 1977, 1978, 1986  Celce-Murcia 1977: child learning English and French simultaneously avoided words containing fricatives in one language by using the word from the other language, e.g. couteau for knife  Well-documented in L1 phonological acquisition and disorders also cleft palate speech, lisp…  Standard OT treatment of avoidance: Null Parse Wrongly predicts phonologically empty output, rather than contentful output that crashes see Orgun and Sprouse 1999, Nevins and Vaux 2004 for further discussion and exemplification

30 Ambiguity and animal wug tests Gallistel, C. 2003. Conditioning from an information processing perspective. Behavioural Processes 61.3:1234 1-13.

31 Consistency  Lombardi 2003: repair of L2 {θ, ð} predictable from structure of L1 system /s, z/: Japanese, France French, German… /t, d/: Russian, Quebecois, Hungarian, Sinhalese… Cf. Ritchie 1968, Nemser 1971, Hancin-Bhatt 1994, Weinberger 1997 )  Actual facts: intra-lingual/individual variation in L1 and L2 acq multiple L1 substitutes for unfamiliar/difficult L2 sound (Hammarberg 1990)  Polish replacement of θ, ð by [s, z] [t, d] or [f, v] (Gussmann 1984:31)  Japanese, German, and Turkish speakers vary in what they hear θ, ð as (Hancin- Bhatt 1994)  Calabrese uses f/v, whereas other Italians use t/d (Flege, Munro, and MacKay 1996) (cf. Cockney vs. New York; Archibald 1998:102)  Unschooled French speakers use [t] (Berger 1951); beginners use [f], intermediate learners use [t] (Wenk 1979); Quebecois use [t], France uses [s] (Archibald 1998:102)  Korean variation between tense [s'] and tense [t'] (think = ssink ~ ttink) (Oh 2002)  Austrian learners of English vary between f ~ s ~ š ~ dental s ~ “lenisized” θ (Wieden 1997:232) Cf. L1:  English L1 acq θ  [f] in stages IV-VII; ð  [d] or [v] in stages V-VII (Wenk 1979, Grunwell 1982)  θ  [f] ~ [s], e.g. Susie’s think  sink ~ fink (Vihman and Greenlee 1987)  Conclusion: ambiguity resolved by arbitrary choice (convention)

32 Dealing with coda [voice]  Overview IL final devoicing as TETU? L1 vs. L2 strategies for dealing with coda [voice] Sources and mechanisms of devoicing

33 Devoicing as TETU?  From Uffmann 2004: 2 guiding principles: 1. Initial state = L1 ranking 2. L2 learners may also assume default M >> F Ranking for lgs that don’t allow codas  *Coda/voi, *Coda >> Faith Ranking for lgs that allow contrastive voicing in codas  Faith >> *Coda/voi, *Coda Demotion of *Coda below Faith based on TL evidence  *Coda/voi >> Faith >> *Coda  [NB requires ignoring evidence for voiced codas] To get Hungarian-English phenomenon (IL devoicing despite both NL and TL having voice contrast in codas), Uffmann proposes L2 learners assume default M >> F until they receive evidence to the contrary  Problem: contravenes OT assumption that L2 learners start with L1 ranking (Pater 2005, etc.)

34  L1: claimed to only use devoicing The “too many solutions problem” (Lombardi 1995, Kager 1999, Steriade 2001, McCarthy 2002…)  Lombardi: MaxOns, *Lar, MaxVoi  Steriade: P-map [McCarthy 2002: targeted constraints can get deletion repair, and therefore shouldn’t be part of OT] Kiparsky 2004: blocking also used (Konni, Meccan Arabic)  L2: epenthesis and deletion attested Deletion  Chinese: Anderson 1983, Xu 2004 Epenthesis  Brazilian Portuguese: Major 1987; Korean: Kang 2003, Iverson and Lee 2004; Vietnamese: Nguyen and Ingram 2004; Chinese: Eckman 1981, Xu 2004 Also found in L1 acq (Major 2001) L1 vs. L2 strategies for coda [voice]

35 More problems with OT analysis of final devoicing  L1 prediction doesn’t fall out nicely from constraint inventory; requires conspiracy of several constraints *Coda/voi-less system (Lombardi 2000, Beckman 2004) misses key articulatory motivation by avoiding (cf. Smith et al. 2005) Lombardi’s scheme should hold for NC as well, but doesn’t (Myers 2002) Lombardi can’t get word-initial neutralization, like in English and Lac Simon  L1 prediction falsified by L2 data  Not clear why devoicing should be easier to learn than contrast  L2 devoicing may not be emergent UG effect: English has devoicing (Haggard 1978, Ladefoged 1982, Pierrehumbert and Talkin 1992, Smith 1997) May be articulatory phonetic problem  “L2 speakers…may not have developed adequate voicing control abilities” (Smith et al. 2005) English-Hungarian case requires reversion to UG ranking, not hidden rankings

36 Levels  Only low-level automatic L1 rules can cause interference (Linell 1979, Rubach 1980, 1984, Broselow 1984, Singh and Ford 1987, Eckman and Iverson 1995) Morphologically-conditioned processes do not cause interference  Problem 1: incoherent in monostratal model  Problem 2: interference from processes treated as morphologically-conditioned in OT (McCarthy 1997, Steriade 2001, Picard 2002, Lombardi 2003) r-insertion in RP pronunciation of L2 French, German, Spanish (Wells 1982)  J’étais déjà[r] ici, ich bin ja[r] fertig, viva[r] España n-insertion in Korean (H. Kim 1999)  [lukεnñu ә sεlp h ] ‘look at yourself’

37  Cases where hidden rankings aren’t involved Idsardi’s 2002 spontaneous chain shift Hungarian-English final devoicing (?) Japanese antepenultimate mora accentuation (?) Hungarian gemination after stressed vowel (?)  Cases where novel marked configurations are produced, violating markedness hierarchy Russian č Odd neutralizations in L1 acq:  Child with dental click for all coronal continuants (Bedore et al. 1994)  Child with ingressives for all postvocalic stops (Gierut and Champion 2000)  4;8 subject, Jason, produces [pfw] “to represent nearly all word-initial liquid clusters, as well as initial labial fricatives” (Edwards 1996:153)  Amahl’s word-initial engma and voiceless sonorants (Smith 1973:4)  child with s in onset, x in coda (Gierut 1993) L2: Yuchun’s production of [ü] for [i] in English UG, phonology, etc.  NB she doesn’t just do it after j Cases where IL phenomenon  NL, TL

38 Conclusions  Classic OT predictions disconfirmed by acquisition data contra M/F-based learning: NDEB, opacity, avoidance contra Consistency: variation, convention contra No levels: Level-based interference, Unnatural interference contra Markedness: Cases where IL phenomenon  NL, TL; Unnatural patterns not harder to learn  Universal markedness/UG plays a role in SLA final devoicing, cluster simplification… Not well captured in OT; SLA requires reference to UG system overridden by L1 and to phenomena not in L1/L2/UG-CON  Conversely, language allows a broader range of possibilities than is countenanced in OT strategies to deal with devoicing, unnatural rules…  Acquisition is generalization-driven and potentially variable supported by DEC effects, conventionalized segmental substitutions, which V to delete in hiatus, deneutralization… Cf. Kiparsky and Menn’s 1977 model, which involves active hypothesis formation and testing on the part of the child, and Fey and Gandour 1979, vs. Stampe’s and OT’s, which are closer to behaviorist stimulus-response For variation, cf. Macken and Ferguson’s flexible learning model that allows for variation, contrary to earlier deterministic models that had predictable L1L2 transfer

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