EVALUATING MODELS OF PARAMETER SETTING

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EVALUATING MODELS OF PARAMETER SETTING

• Janet Dean Fodor
• Graduate Center,
• City University of New York

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(No Transcript)
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On behalf of CUNY-CoLAGCUNY Computational
Language Acquisition GroupWith support from
PSC-CUNY

• William G. Sakas, co-director
• Carrie Crowther Lisa Reisig-Ferrazzano
  
• Atsu Inoue Iglika
  Stoyneshka-Raleva
• Xuan-Nga Kam Virginia Teller
• Yukiko Koizumi Lidiya Tornyova
• Eiji Nishimoto Erika Troseth
• Artur Niyazov Tanya Viger
• Iana Melnikova Pugach Sam Wagner

www.colag.cs.hunter.cuny.edu
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Before we start

• Warning I may skip some slides.
• But not to hide them from you.
• Every slide is at our website
  www.colag.cs.hunter.cuny.edu

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What we have done

• A factory for testing models of parameter
  setting.
• UG 13 parameter values ? 3,072 languages
  (simplified but human-like).
• Sentences of a target language are the input to
  a learning model.
• Is learning successful? How fast?
• Why?

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Our Aims

• A psycho-computational model of syntactic
  parameter setting.
• Psychologically realistic.
• Precisely specified.
• Compatible with linguistic theory.
• And it must work!

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Parameter setting as the solution (1981)

• Avoids problems of rule-learning.
• Only 20 (or 200) facts to learn.
• Triggering is fast automatic no linguistic
  computation is necessary.
• Accurate.
• BUT This has never been modeled.

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Parameter setting as the problem
(1990s)

• R. Clark, and Gibson Wexler have shown
• P-setting is not labor-free, not always
  successful. Because ? The parameter
  interaction problem. ? The parametric
  ambiguity problem.
• Sentences do not tell which parameter values
  generated them.

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This evening

• Parameter setting
• How severe are the problems?
• Why do they matter?
• How to escape them?
• Moving forward from problems to
  explorations.

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Problem 1 Parameter interaction

• Even independent parameters interact in
  derivations (Clark 1988,1992).
• Surface string reflects their combined effects.
• So one parameter may have no distinctive
  isolatable effect on sentences. no trigger,
  no cue (cf. cue-based learner
  Lightfoot 1991 Dresher 1999)
• Parametric decoding is needed. Must disentangle
  the interactions, to identify which p-values a
  sentence requires.

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Parametric decoding

• Decoding is not instantaneous. It is hard work.
  Because
• To know that a parameter value is necessary,
  must test it in company of all other p-values.
• So whole grammars must be tested against the
  sentence. (Grammar-testing ? triggering!)
• All grammars must be tested, to identify one
  correct p-value. (exponential!)

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Decoding

• This sets no wh-movt, p-stranding, head initial
  VP, V to I to C, no affix hopping, C- initial,
  subj initial, no overt topic marking
• Doesnt set oblig topic, null subj, null topic

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More decoding

• AdvWH P NOT Verb S KA.
• This sets everything except overt topic marking.
• VerbFIN.
• This sets nothing, not even null subject.

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Problem 2 Parametric ambiguity

• A sentence may belong to more than one language.
• A p-ambiguous sentence doesnt reveal thetarget
  p-values (even if decoded).
• Learner must guess ( inaccurate) or
  pass ( slow, when? )
• How much p-ambiguity is there in natural
  language? Not quantified probably vast.

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Scale of the problem (exponential)

• P-interaction and p-ambiguity are likely to
  increase with the of parameters.
• How many parameters are there?

20 parameters ? 220 grammars over a
million 30 parameters ? 230 grammars over a
billion 40 parameters ? 240 grammars over a
trillion 100 parameters ? 2100 grammars ???
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Learning models must scale up

• Testing all grammars against each input sentence
  is clearly impossible.
• So research has turned to search methods how to
  sample and test the huge field of grammars
  efficiently.
• ? Genetic algorithms (e.g., Clark 1992)
  ? Hill-climbing algorithms (e.g., Gibson
  Wexlers TLA 1994)

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Our approach

• Retain a central aspect of classic triggering
  Input sentences guide the learner toward the
  p-values they need.
• Decode on-line parsing routines do the work.
  (Theyre innate.)
• Parse the input sentence (just as adults do, for
  comprehension) until it crashes.
• Then the parser draws on other p-values, to find
  one that can patch the parse-tree.

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Structural Triggers Learners (CUNY)

• STLs find one grammar for each sentence.
• More than that would require parallel parsing,
  beyond human capacity.
• But the parser can tell on-line if there is
  (possibly) more than one candidate.
• If so guess, or pass (wait for unambig).
• Considers only real candidate grammarsdirected
  by what the parse-tree needs.

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Summary so far

• Structural triggers learners (STLs) retain an
  important aspect of triggering (p-decoding).
• Compatible with current psycholinguistic models
  of sentence processing.
• Hold promise of being efficient. (Home in on
  target grammar, within human resource limits.)
• Now Do they really work, in a domain
  with realistic parametric ambiguity?

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Evaluating learning models

• Do any models work?
• Reliably? Fast? Within human resources?
• Do decoding models work better than domain-search
  (grammar-testing) models?
• Within decoding models, is guessing better or
  worse than waiting?

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Hope it works! If not

• The challenge What is UG good for?
• All that innate knowledge, only a few facts to
  learn, but you cant say how!
• Instead, one simple learning procedure? Adjust
  the weights in a neural network ? Record
  statistics of co-occurrence frequencies.
• Nativist theories of human language are
  vulnerable until some UG-based learner is shown
  to perform well.

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Non-UG-based learning

• Christiansen, M.H., Conway, C.M. and Curtin, S.
  (2000). A connectionist single-mechanism account
  of rule-like behavior in infancy. In Proceedings
  of 22nd Annual Conference of Cognitive Science
  Society, 83-88. Mahwah, NJ Lawrence Erlbaum.
• Culicover, P.W. and Nowak, A. (2003) A Dynamical
  Grammar. Oxford, UK Oxford University Press.
  Vol.Two of Foundations of Syntax.
• Lewis, J.D. and Elman, J.L. (2002) Learnability
  and the statistical structure of language
  Poverty of stimulus arguments revisited. In B.
  Skarabela et al. (eds) Proceedings of BUCLD 26,
  Somerville, Mass Cascadilla Press.
• Pereira, F. (2000) Formal Theory and Information
  theory Together again? Philosophical
  Transactions of the Royal Society, Series A 358,
  1239-1253.
• Seidenberg, M.S., MacDonald, M.C. (1999) A
  probabilistic constraints approach to language
  acquisition and processing. Cognitive Science 23,
  569-588.
• Tomasello, M. (2003) Constructing a Language A
  Usage-Based Theory of Language Acquisition.
  Harvard University Press.

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The CUNY simulation project

• We program learning algorithms proposed in the
  literature. (12 so far)
• Run each one on a large domain of human-like
  languages. 1,000 trials (?1,000 children) each.
• Success rate of trials that identify target.
• Speed average of input sentences consumed
  until learner has identified the target grammar.
• Reliability/speed of input sentences for 99
  of trials (? 99 of children) to attain the
  target.
• Subset Principle violations and one-step local
  maxima excluded by fiat. (Explained below as
  necessary.)

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Designing the language domain

• Realistically large, to test which models scale
  up well.
• As much like natural languages as possible.
• Except, input limited like child-directed speech.
• Sentences must have fully specified tree
  structure(not just word strings), to test models
  like STL.
• Should reflect theoretically defensible
  linguistic analyses (though simplified).
• Grammar format should allow rapid conversion into
  the operations of an effective parsing device.

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Language domains created
params langs sents per lang tree structure Language properties
Gibson Wexler (1994) 3 8 12 or 18 Not fully specified Word order V2
Bertolo et al. (1997) 7 64 distinct Many Yes GW V-raising degree-2
Kohl (1999) 12 2,304 Many Partial B et al. scrambling
Sakas Nishi-moto (2002) 4 16 12-32 Yes GW null subj/topic
Fodor, Melni-kova Troseth (2002) 13 3,072 168-1,420 Yes SN Imp wh-movt piping etc
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Selection criteria for our domain

• We have given priority to syntactic phenomena
  which
• Occur in a high proportion of known natl langs
• Occur often in speech directed to 2-3 year olds
• Pose learning problems of theoretical interest
• A focus of linguistic / psycholinguistic
  research
• Syntactic analysis is broadly agreed on.

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By these criteria

• Questions, imperatives.
• Negation, adverbs.
• Null subjects, verb movement.
• Prep-stranding, affix-hopping (though not
  widespread!).
• Wh-movement, but no scrambling yet.

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Not yet included

• No LF interface (cf. Villavicencio 2000)
• No ellipsis no discourse contexts to license
  fragments.
• No DP-internal structure Case agreement.
• No embedding (only degree-0).
• No feature checking as implementation of movement
  parameters (Chomsky 1995ff.)
• No LCA / Anti-symmetry (Kayne 1994ff.)

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Our 13 parameters (so far)

• Parameter
  Default
• Subject Initial (SI) yes
• Object Final (OF) yes
• Complementizer Initial (CI) initial
• V to I Movement (VtoI) no
• I to C Movement (of aux or verb) (ItoC)
  no
• Question Inversion (Qinv I to C in questions
  only) no
• Affix Hopping (AH) no
• Obligatory Topic (vs. optional) (ObT)
  yes
• Topic Marking (TM) no
• Wh-Movement obligatory (vs. none) (Wh-M)
  no
• Pied Piping (vs. preposition stranding) (PI)
  piping
• Null Subject (NS) no
• Null Topic (NT) no

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Parameters are not all independent

• Constraints on P-value combinations
• If ObT then - NS.
• (A topic-oriented language does not have
  null subjects.)
• If - ObT then - NT.
• (A subject-oriented language does not have
  null topics.)
• If VtoI then - AH.
• (If verbs raise to I, affix hopping does
  not occur.)
• (This is why only 3,072 grammars, not 8,192.)

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Input sentences

• Universal lexicon S, Aux, O1, P, etc.
• Input is word strings only, no structure.
• Except, the learner knows all word categories and
  all grammatical roles!
• Equivalent to some semantic boot-strapping no
  prosodic bootstrapping (yet!)

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Learning procedures

• In all models tested (unless noted), learning is
• Incremental hypothesize a grammar after each
  input. No memory for past input.
• Error-driven if Gcurrent can parse the
  sentence, retain it.
• Models differ in what the learner does when
  Gcurrent fails grammar change is needed.

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The learning models preview

• Learners that decode STLs. ? Waiting
  (squeaky clean) ? Guessing
• Grammar-testing learners ? Triggering Learning
  Algorithm (GW) ? Variational Learner (Yang
  2000)
• plus benchmarks for comparison ? too powerful
  ? too weak

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Learners that decode STLs

• Strong STL Parallel parse input sentence, find
  all successful grammars. Adopt p-values they
  share. (A useful benchmark, not a psychological
  model.)
• Waiting STL Serial parse. Note any choice-point
  in the parse. Set no parameters after a choice.
  (Never guesses. Needs fully unambig triggers.)
  (Fodor 1998a)
• Guessing STLs Serial. At a choice-point,
  guess.(Can learn from p-ambiguous input.)
  (Fodor 1998b)

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Guessing STLs guessing principles

• If there is more than one new p-value that
  could patch the parse tree
• Any Parse Pick at random.
• Minimal Connections Pick the p-value that
  gives the simplest tree. (? MA LC)
• Least Null Terminals Pick the parse with
  the fewest empty categories. (? MCP)
• Nearest Grammar Pick the grammar that
  differs least from Gcurrent.

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Grammar-testing TLA

• Error-driven random Adopt any grammar.
  (Another baseline not a psychological model.)
• TLA (Gibson Wexler, 1994) Change any one
  parameter. Try the new grammar on the sentence.
  Adopt it if the parse succeeds. Else pass.
• Non-greedy TLA (Berwick Niyogi, 1996) Change
  any one parameter. Adopt it. (No test of new
  grammar against the sentence.)
• Non-SVC TLA (BN 96) Try any grammar other than
  Gcurrent. Adopt it if the parse succeeds.

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Grammar-testing models with memory

• Variational Learner (Yang 2000,2002) has memory
  for success / failure of p-values.
• A p-value is? rewarded if in a grammar that
  parsed an input ? punished if in a grammar that
  failed.
• Reinforcement is approximate, because of
  interaction. A good p-value in a bad grammar is
  punished, and vice versa.

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With memory Error-driven VL

• Yangs VL is not error-driven. It chooses
  p-values with probability proportional to their
  current success weights. So it occasionally tries
  out unlikely p-values.
• Error-driven VL (Sakas Nishimoto, 2002) Like
  Yangs original, but
• First, set each parameter to its currently
  more successful value. Only if that fails, pick
  a different grammar as above.

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Previous simulation results

• TLA is slower than error-driven random on the GW
  domain, even when it succeeds (Berwick Niyogi
  1996).
• TLA sometimes performs better, e.g., in strongly
  smooth domains (Sakas 2000, 2003).
• TLA fails on 3 of GWs 8 languages, and on 95.4
  of Kohls 2,304 languages.
• There is no default grammar that can avoid TLA
  learning failures. The best starting grammar
  succeeds only 43 (Kohl 1999).
• Some TLA-unlearnable languages are quite natural,
  e.g., Swedish-type settings (Kohl 1999).
• Waiting-STL is paralyzed by weakly equivalent
  grammars (Bertolo et al. 1997).

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Data by learning model
Algorithm failure rate inputs (99 of trials) inputs (average)
Error-driven random 0 16,663 3,589
TLA original 88 16,990 961
TLA w/o Greediness 0 19,181 4,110
TLA without SVC 0 67,896 11,273
Strong STL 74 170 26
Waiting STL 75 176 28
Guessing STLs
Any parse 0 1,486 166
Minimal Connections 0 1,923 197
Least Null Terminals 0 1,412 160
Nearest Grammar 80 180 30
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Summary of performance

• Not all models scale up well.
• Squeaky-clean models (Strong / Waiting
  STL)fail often. Need unambiguous triggers.
• Decoding models which guess are most efficient.
• On-line parsing strategies make good learning
  strategies. (?)
• Even with decoding, conservative domain search
  fails often (Nearest Grammar STL).
• Thus Learning-by-parsing fulfills its promise.
  Psychologically natural triggering is
  efficient.

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Now that we have a workable model

• Use it to investigate questions of interest
• Are some languages easier than others?
• Do default starting p-values help?
• Does overt morphological marking facilitate
  syntax learning?
• etc..
• Compare with psycholinguistic data, where
  possible. This tests the model further, and may
  offer guidelines for real-life studies.

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Are some languages easier?
Guessing STL- MC inputs (99 of trials) inputs (average)
Japanese ? 87 21
French 99 22
German 727 147
English ? 1,549 357
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What makes a language easier?

• Language difficulty is not predicted by how many
  of the target p-settings are defaults.
• Probably what matters is parametric ambiguity
• Overlap with neighboring languages
• Lack of almost-unambiguous triggers
• Are non-attested languages the difficult ones?
  (Kohl, 1999 explanatory!)

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Sensitivity to input properties

• How does the informativeness of the input affect
  learning rate?
• Theoretical interest To what extent can
  UG-based p-setting be input-paced?
• If an input-pacing profile does not match child
  learners, that could suggest biological timing
  (e.g., maturation).

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Some input properties

• Morphological marking of syntactic features ?
  Case ? Agreement ? Finiteness
• The target language may not provide them.
  Or the learner may not know them.
• Do they speed up learning? Or just create
  more work?

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Input properties, contd

• For real children, it is likely that
• Semantics / discourse pragmatics signals
  illocutionary force ILLOC DEC, ILLOC Q
  or ILLOC IMP
• Semantics and/or syntactic context reveals SUBCAT
  (argument structure) of verbs.
• Prosody reveals some phrase boundaries(as well
  as providing illocutionary cues).

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Making finiteness audible

• /-FIN distinguishes Imperatives from
  Declaratives. (So does ILLOC, but its
  inaudible.)
• Imperatives have null subject. E.g., Verb O1.
• A child who interprets an IMP input as a DEC
  could mis-set NS for a -NS lang.
• Does learning become faster / more accurate when
  /-FIN is audible? No. Why not?
• Because Subset Principle requires learner to
  parse IMP/DEC ambiguous sentences as IMP.

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Providing semantic info ILLOC

• Suppose real children know whether an input is
  Imperative, Declarative or Question.
• This is relevant to ItoC vs. Qinv. (
  Qinv ? ItoC only in questions )
• Does learning become faster / more accurate when
  ILLOC is audible? No. Its slower!
• Because its just one more thing to learn.
• Without ILLOC, a learner could get allword
  strings right, but their ILLOCs and p-values all
  wrong and count as successful.

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Providing SUBCAT information

• Suppose real children can bootstrap verb
  argument structure from meaning / local context.
• This can reveal when an argument is missing.
  How can O1, O2 or PP be missing? Only by NT.
• If NT then also ObT and -NS (in our
  UG).
• Does learning become faster / more accurate
  when learners know SUBCAT? Yes. Why?
• SP doesnt choose between no-topic and null-
  topic. Other triggers are rare. So triggers for
  NT are useful.

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Enriching the input Summary

• Richer input is good if it helps with something
  that must be learned anyway ( other cues are
  scarce).
• It hinders if it creates a distinction that
  otherwise could have been ignored. (cf. Wexler
  Culicover 1980)
• Outcomes depend on properties of this domain,
  but it can be tailored to the issue at hand.
• The ultimate interest is the light these data
  shed on real language acquisition.
• ? We can provide profiles of UG-based /
  input- (in)sensitive learning, for
  comparison with children.
• The outcomes are never quite as anticipated.

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This is just the beginning

• Next on the agenda ???

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Next steps input properties

• How much damage from noisy input? E.g., 1
  sentence in 5 / 10 / 100 not from target
  language.
• How much facilitation from starting
  small?E.g., Probability of occurrence inversely
  proportional to sentence length.
• How much facilitation (or not) from the exact mix
  of sentences in child-directed speech? (cf.
  Newport, 1977 Yang, 2002)

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Next steps learning models

• Add connectionist and statistical learners.
• Add our favorite STL ( Parse Naturally), with
  MA, MCP etc. and a p-value lexicon.
  (Fodor 1998b)
  
• Implement the ambiguity / irrelevance
  distinction, important to Waiting-STL.
• Evaluate models for realistic sequence of
  setting parameters. (Time course data)
• Your request here ?

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www.colag.cs.hunter.cuny.edu
www.colag.cs.hunter.cuny.edu

• The end

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REFERENCES

• Bertolo, S., Broihier, K., Gibson, E., and
  Wexler, K. (1997) Cue-based learners in
  parametric language systems Application of
  general results to a recently proposed learning
  algorithm based on unambiguous 'superparsing'. In
  M. G. Shafto and P. Langley (eds.) 19th Annual
  Conference of the Cognitive Science Society,
  Lawrence Erlbaum Associates, Mahwah, NJ.
• Berwick, R.C. and Niyogi, P. (1996) Learning from
  Triggers. Linguistic Inquiry, 27(2), 605-622.
• Chomsky, N. (1995) The Minimalist Program.
  Cambridge MA MIT Press.
• Clark, R. (1988) On the relationship between the
  input data and parameter setting. NELS 19, 48-62.
• Clark, R. (1992) The selection of syntactic
  knowledge, Language Acquisition 2(2), 83-149.
• Dresher, E. (1999) Charting the learning path
  Cues to parameter setting. Linguistic Inquiry
  30.1, 27-67.
• Fodor, J D. (1998a) Unambiguous triggers,
  Linguistic Inquiry 29.1, 1-36.
• Fodor, J.D. (1998b) Parsing to learn. Journal of
  Psycholinguistic Research 27.3, 339-374.
• Fodor, J.D., I. Melnikova and E. Troseth (2002) A
  structurally defined language domain for testing
  syntax acquisition models, CUNY-CoLAG Working
  Paper 1.
• Gibson, E. and Wexler, K. (1994) Triggers.
  Linguistic Inquiry 25, 407-454.
• Kayne, R.S. (1994) The Antisymmetry of Syntax.
  Cambridge MA MIT Press.
• Kohl, K.T. (1999) An Analysis of Finite
  Parameter Learning in Linguistic Spaces. Masters
  Thesis, MIT.
• Lightfoot, D. (1991) How to set parameters
  Arguments from Language Change. Cambridge, MA
  MIT Press.
• Sakas, W.G. (2000) Ambiguity and the
  Computational Feasibility of Syntax Acquisition,
  PhD Dissertation, City University of New York.
• Sakas, W.G. and Fodor, J.D. (2001). The
  Structural Triggers Learner. In S. Bertolo (ed.)
  Language Acquisition and Learnability. Cambridge,
  UK Cambridge University Press.
• Sakas, W.G. and Nishimoto, E. (2002). Search,
  Structure or Heuristics? A comparative study of
  memoryless algorithms for syntax acquisition.
  24th Annual Conference of the Cognitive Science
  Society. Hillsdale, NJ Lawrence Erlbaum
  Associates.
• Yang, C.D. (2000) Knowledge and Learning in
  Natural Language. Doctoral dissertation, MIT.
• Yang, C.D. (2002) Knowledge and Learning in
  Natural Language. Oxford University Press.
• Villavicencio, A. (2000) The use of default
  unification in a system of lexical types. Paper
  presented at the Workshop on Linguistic Theory
  and Grammar Implementation, Birmingham,UK.

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Something we cant do production

• What do learners say when they dont know?
• ? Sentences in Gcurrent, but not in Gtarget.
• Do these sound like baby-talk?
• Me has Mary not kissed why? (early)
  Whom must not take candy from? (later)
• ? Sentences in Gtarget but not in Gcurrent.
• Goblins Jim gives apples to.

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CHILD-DIRECTED SPEECH STATISTICS FROM THE CHILDES
DATABASE

• The current domain of 13 parameters is almost as
  much as its feasible to work with maybe we can
  eventually push it up to 20.
• Each language in the domain has only the
  properties assigned to it by the 13 parameters.
• Painful decisions what to include? what to
  omit?
• To decide, we consult adult speech to children in
  CHILDES transcripts. Child age approx 1½ to 2½
  years (earliest produced syntax).
• Childs MLU very approx 2. Adults MLU from 2.5
  to 5.
• So far English, French, German, Italian,
  Japanese.
• (Child Language Data Exchange System,
  MacWhinney 1995)

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STATISTICS ON CHILD-DIRECTED SPEECH FROM THE
CHILDES DATABASE


  ENGLISH GERMAN ITALIAN JAPANESE RUSSIAN
NameAge (YM.D) Eve 18-9.0 Nicole 18.15 Martina 18.2 Jun (22.5-25) Varvara 16.5 -17.13
File Name eve05-06.cha nicole.cha mart03,08.cha jun041-044.cha varv01-02.cha
Researcher/Childes folder name BROWN WAGNER CALAMBRONE ISHII PROTASSOVA
Number of Adults 4,3 2 2,2 1 4
MLU Child 2.13 2.17 1.94 1.606 2.8
MLU Adults (avg. of all) 3.72 4.56 5.1 2.454 3.8
Total Utterances (incl. Frags.) 1304 1107 1258 1691 1008
Usable Utterances/Fragments 806/498 728/379 929/329 1113/578 727/276
USABLES ( of all utterances) 62 66 74 66 72
DECLARATIVES 40 42 27 25 34
DEICTIC DECLARATIVES 8 6 3 8 7
MORPHO-SYNTACTIC QUESTIONS 10 12 0 18 2
PROSODY-ONLY QUESTIONS 7 5 15 14 5
WH-QUESTIONS 22 8 27 15 34
IMPERATIVES 13 27 24 11 11
EXCLAMATIONS 0 0 1 3 0
LET'S CONSTRUCTIONS 0 0 2 4 2
60
FRAGMENTS ( of all utterances) 38 34 26 34 27
NP FRAGMENTS 25 24 37 10 35
VP FRAGMENTS 8 7 6 1 8
AP FRAGMENTS 4 3 16 1 7
PP FRAGMENTS 9 4 5 1 3
WH-FRAGMENTS 10 2 10 2 6
OTHER (E.g. stock expressions yes, huh) 44 60 26 85 41
COMPLEX NPs (not from fragments)          
Total Number of Complex NPs 140 55 88 58 105
Approx 1 per 'n' utterances 6 13 11 19 7
NP with one ADJ 91 36 27 38 54
NP with two ADJ 7 1 2 0 4
NP with a PP 20 3 15 14 18
NP with possessive ADJ 22 7 0 0 4
NP modified by AdvP 0 0 31 1 6
NP with relative clause 0 8 13 5 5
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DEGREE-n Utterances          
DEGREE 0 88 84 81 94 77
Degree 0 deictic (E.g. that's a duck) 8 6 2 8 18
Degree 0 (all others) 92 94 98 92 82
DEGREE 1 12 16 19 6 33
infinitival complement clause 36 1 31 2 30
finite complement clause 12 1 40 10 26
relative clause 10 16 12 8 3
coordinating clause 30 59 9 10 41
adverbial clause 11 18 7 80 0
ANIMACY and CASE          
Utt. with animate somewhere 62 60 37 8 31
Subjects (overt) 94 91 56 63 97
Objects (overt) 18 23 44 23 14
Case-marked NPs 238 439 282 100 949
Nominative 191 283 36 45 552
Accusative 47 79 189 4 196
Dative 0 77 57 4 35
Genitive 0 0 0 14 98
Topic 0 0 0 34 0
Instrumental and Prepositional 0 0 0 0 68
Subject drop 0 26 379 740 124
Object drop 0 4 0 125 37
Negation Occurrences 62 73 43 72 71
Nominal 5 19 2 0 8
Sentential 57 54 41 72 63
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