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Title: Adventures with Camille, a Computational Simulation of a Minimalist Language Learner


1
Adventures with Camille, a Computational
Simulation of a Minimalist Language Learner
  • Peter W. Culicover (The Ohio State University)
  • collaborating with
  • Andrzej Nowak (Warsaw / FAU )
  • Wojciech Borkowski (Warsaw)
  • Piotr Kochanski (Warsaw)

2
  • Thanks to the James S. Mcdonnell Foundation, the
    Center for Cognitive Science at OSU the Center
    for Complex Systems at U. of Warsaw for their
    support.

3
Questions
  • How should we think about what knowledge of
    language consists of?
  • What is the architecture of the learner that
    explains how we have this knowledge?
  • Can we simulate language acquisition to explore
    this view?

4
Outline
  • Perspective Simpler Syntax the
    Constructionalist Manifesto
  • Facts Syntactic Nuts and the Architecture of the
    Language Faculty
  • Learner CAMiLLe and Dynamical Grammar
    computational simulation
  • Social network simulation

5
Perspectives
  • On the standard view in linguistic theory, a
    generative grammar of a language is an idealized
    characterization of the linguistic knowledge, or
    linguistic competence, of an idealized native
    speaker.
  • A theory of generative grammar in turn is a
    characterization of possible generative grammars.

6
The standard view
  • On this view, a generative grammar is a theory
    something that really exists, the linguistic
    competence of the native speaker.
  • And the theory of grammar is a theory about the
    language faculty that explains why linguistic
    competence takes the form that it does.
  • Both the grammar and the theory of grammar are in
    some sense "in the mind" and form a part of the
    explanation of human linguistic abilities.

7
An alternative view
  • What resides in the language faculty is actually
    something of a different sort that reflects in
    its architecture the dynamical character of
    language.
  • In particular, it directly reflects the fact that
    the grammar is acquired over time, that it is a
    psychological mechanism used for speaking and
    understanding, and that it undergoes change over
    time.

8
The challenge...
  • ...for an approach to human language that does
    not incorporate a standard version of generative
    grammar is to account for the linguistic
    phenomena themselves.
  • Such an alternative must be able to explain what
    generative grammar explains, and more.

9
The challenge, contd
  • Minimally, the linguistic content of the
    alternative formulation must be at least
    equivalent to the content of the generative
    grammar.
  • That is, both must provide the same (or
    equivalent) empirically adequate structural
    descriptions of words, phrases, idioms,
    constructions and sentences.
  • Moreover, such an account should explain the
    dynamical properties of language.

10
A Constructionist Manifesto
  • The Neural Basis of Cognitive Development A
    Constructionist Manifesto. S. R. Quartz and T. J.
    Sejnowski. 1997. Behavioural and Brain Sciences
    20(4) 537 -596.
  • In contrast to learning as selective induction,
    the central component of the constructivist model
    is that it does not involve a search through an a
    priori defined hypothesis space, and so is not an
    instance of model-based estimation, or parametric
    regression.  Instead, the constructivist learner
    builds this hypothesis as a process of
    activity-dependent construction of the
    representations that underlie mature skills.

11
Constructionalism
  • We adopt a Jackendoffian perspective on grammar
    (e.g. Culicover, Syntactic Nuts, Oxford, 1999
    Jackendoff, Foundations of Language, Oxford,
    2001 Culicover Jackendoff, Simpler Syntax
    Oxford, to appear), which is a constructionalist
    one
  • a. The job of grammar is to describe the
    sound-meaning correspondences.
  • b. Some of these correspondences are
    unanalyzable (words).
  • c. Some have linguistic structure but are simple
    or not entirely transparent on the meaning side
    (idioms) (no nice structure/meaning matchups).
  • d. Some have structure and are transparent on
    the meaning side (compositional semantics
    interpreting canonical phrase structure).
  • e. Some are a combination of the above
    ('constructions'), ranging from quasi-idioms,
    double-objects, movement along a path
    expressions, syntactic nuts (see above), various
    operator-trace binding constructions, etc. Each
    has some degree of predictability and generality,
    and some idiosyncrasies.

12
Simple Syntax
  • Simple(r) Syntax Hypothesis (SSH)
  • The most explanatory syntactic theory is one that
    imputes the minimum structure necessary to
    mediate between sound and meaning.

13
The Correspondence Spectrum
  • Ill go through these quickly just to give you a
    feel for what Im talking about...

14
Words
  • Many words are unanalyzable correspondences
    between sound and meaning.
  • (although some (e.g. Hale Keyser) have argued
    that apparently simple words are syntactically
    complex and are the product of derivations
    involving movement and deletion.)
  • (but the relations captured by such derivations
    can be captured in non-derivational
    (constructionist) ways, and the latter are
    required for certain aspects of the
    correspondences.)

15
Idioms
  • Idioms have recognizable syntactic structure but
    unpredictable meaning
  • by and large
  • lo and behold
  • beat a dead horse
  • make amends
  • cast aspersions on (at / to)
  • a flash in the pan
  • put up with

16
VP constructional idioms
  • a. Way-construction (Jackendoff 1990, Goldberg
    1995)
  • Elmer hobbled/laughed/joked his way to the
    bank.
  • (? Elmer went/made his way to the bank
    hobbling/laughing /joking)
  • b. Time-away construction (Jackendoff 1997b)
  • Hermione slept/drank/sewed/programmed three
    whole evenings away.
  • (? Hermione spent three whole evenings
    sleeping/drinking/sewing /programming)
  • c. Soundmotion construction (Levin and
    Rappaport Hovav 1995)
  • The car whizzed/rumbled/squealed past Harry.
  • (? the car went past Harry, making
    whizzing/rumbling/squealing noises)
  • d. Resultative construction
  • The chef cooked the pot black.
  • (? the chef made the pot black by cooking
    in/with it)

17
Syntax-semantics mismatches
  • All of these constructions share the same basic
    syntax (not surprisingly, since they are all
    English) what is idiosyncratic is the way in
    which their meanings are related to the meanings
    of the parts and to the structure in which they
    (the parts) appear.

18
Motto Construction of language produces
constructions in language
  • which means...
  • as knowledge of language is constructed
    dynamically by a learner,
  • what emerges are constructions that may
    ultimately become rules, but only if given
    enough evidence and a suitable generalization
    mechanism,
  • otherwise, they remain constructions.

19
How does the learner know what she/he is dealing
with?
  • Since there is no way for the learner to know
    where on the spectrum a correspondence really is,
    the conservative strategy is to start at the
    word/idiom end, and then move away as the weight
    of the evidence warrants generalization.
    (Tomasello)

20
CAMiLLe
  • C onservative (or Concrete)
  • (dont generalize much beyond the evidence)
  • A ttentive
  • (all input is potentially relevant)
  • Mi nimalist
  • L anguage
  • Le arner

21
Goals of CAMiLLe
  • Pursuing the logic of Concrete Minimalism (and
    Simpler Syntax), we constructed CAMiLLe with
    minimal prior knowledge of linguistic structure.
  • Language acquisition by CAMiLLe is intended to
    simulate the formation of trajectories and flows,
    and self-organization, in a dynamical system.
  • Our experiments with CAMiLLe are intended to
    determine how much grammatical knowledge such a
    minimalist learner is capable of acquiring
    strictly from sound/meaning pairings.

22
The spatial metaphorRegions categories/features
(slice of a many dimensional space)
  • Sound/meaning pairs are computed as the system
    changes states, represented as trajectories in a
    space.
  • Structure guides movement from one region of the
    space to another.

23
N -gt A N
24
(No Transcript)
25
N -gt A N
Recursion
26
Flow
  • Multiple trajectories from one region to another
    create flows.
  • If the flow is restricted in the space it is a
    construction.

27
Flow -gt Rule
  • The individual trajectories may carve out a broad
    region of the entire trajectory space.
  • If there are enough of them, we could fill in the
    empty spaces make a rule.

28
Self-organization
  • CAMiLLe, as a dynamical system, should
    self-organize when it is possible to collapse
    individual rules or representations. This
    produces generalization and over-generalization.
  • (Self-organization is limited in the current
    implementation of CAMiLLe, it should be noted.)

29
Concrete minimalism
  • The computational system should be maximally
    simple
  • not in terms of abstract computational
    simplicity,
  • but in terms of the criterion of
  • learning on the basis of the concrete
    evidence.
  • That is, it should be the simplest system that
    can arrive at an adequate account of the language
    given a large but finite sample of experience.
    (Simpler Syntax)

30
Strategy
  • Pursuing the logic of Concrete Minimalism, we
    constructed CAMiLLe with minimal prior knowledge
    of linguistic structure.
  • Our experiments with CAMiLLe are intended to
    determine how much grammatical knowledge such a
    minimalist learner is capable of acquiring.

31
  • Success would be nice, but some failure can be
    quite informative.

32
A Concrete Minimalist Learner
  • We assume that the learner has access to
  • sounds and the phonological system
  • meanings and the Conceptual Structure system
  • the minimal prior information about grammar that
    is necessary to acquire a descriptively adequate
    grammar given the paired sounds and meanings of a
    language.
  • To begin, we assume, counterfactually, that this
    information is ZERO, and see what needs to be
    added.

33
Representations in CAMiLLe
  • There are two representational systems in
    CAMiLLe
  • the system that encodes meaning,
  • and the system that encodes form.
  • Meaning is encoded as a structured list of
    arguments and adjuncts, where thematic roles and
    modifiers are explicitly specified.
  • Syntax (form) is simply the linear arrangement of
    elements (words and morphemes)

34
Meaning
  • A meaning in the CS presented to CAMiLLe is
    expressed in a simple attribute-value language.
    E.g.,
  • TOUCH(AGENTMAN,THEMEANIMAL)
  • Relations typically expressed by verbs are
    represented as constants with an associated
    argument structure.
  • Arguments are given as thematic roles with their
    values. (Like AGENTMAN)
  • We assume that the meaning that CAMiLLe is
    presented with contains only primitives that are
    cognitively accessible to CAMiLLe at a given
    stage of development.

35
Cognitive Development
  • At the earliest stage of development CAMiLLe
    (simulating an actual child) may only perceive
    that some man touches some animal.
  • For example, John touches the cat could have the
    meaning
  • TOUCH(AGENTMAN,THEMEANIMAL)
  • at an early stage.

36
  • Meanings become more sophisticated as a
    consequence of development of cognition and
    perception.
  • E.g., later, the learner may perceive that there
    is John, a distinct male person, that there is a
    particular type of animal (a cat), that both are
    singular in this context, and that they
    participate in this relation.
  • TOUCH(AGENTJOHN(TYPEPERSON,
  • GENDERMALE,NUMSG),
  • THEMECAT(TYPEANIMAL,NUMSG))

37
Capacities of CAMiLLe
  • CAMiLLe is conservative, in that it does not form
    hypotheses for which it does not have some
    evidence.
  • In this implementation, CAMiLLe is assumed to
    have prior knowledge of what the words are in a
    sentence. It does not perform word segmentation
    (although in principle it could).

38
Knowledge about categories
  • CAMiLLe must know that lexical categories exist
    (but not which ones) and tries to determine what
    categories there are (that is, which words are
    similar in syntactic, semantic or morphological
    characteristics).
  • CAMiLLe will generalize elements into a category
    when it appears that they share sufficiently many
    characteristics.
  • Distributional characteristics
  • Similar meaning (e.g. cat dog both refer to
    similar animals)
  • CAMiLLe does not know about the specific
    syntactic categories such as Noun, Verb,
    Adjective, etc.

39
Knowledge about structure
  • CAMiLLe must know that there are heads and
    phrases and knows that a phrase consists of at
    least a head and possibly other material that
    bears some relationship to it.
  • there does not appear to be evidence in the raw
    data that would tell you that there are such
    things if you werent looking for them.
  • Syntactic Nuts (Culicover 1999) A lot of core
    syntactic structure is in CS. Unpredictable
    structure and particular linear relationships are
    in the correspondence rules specific to the
    language.

40
Ignorance is bliss
  • CAMiLLe is a concrete minimalist, in that it only
    makes use of information about the linear order
    of formatives and corresponding meanings
    presented to it in the course of learning.
  • CAMiLLe does not know about functional heads.
    There are no purely grammatical formatives in
    CAMiLLes implementation that CAMiLLe tries to
    match against linguistic input.
  • CAMiLLe does not know about transformations per
    se.
  • And lots more...

41
More bliss
  • CAMiLLe does not know about traces or other empty
    categories (but should, and will, in the next
    implementation).
  • CAMiLLe does not have grammatical indices.
  • CAMiLLe does not know about constraints, such as
    Subjacency and the ECP, and in fact lacks all
    comparable prior knowledge of syntactic theory.
  • Where do these come from?

42
Even more bliss
  • CAMiLLe cannot compute morphological structure,
    and must have morphological structure presented
    to it explicitly in order to make use of it in
    forming grammatical hypotheses.
  • CAMiLLe does not know about classical X-bar
    theory. That is, CAMiLLe does not know about
    specifiers and complements per se, zero-level and
    maximal projections, and so on.
  • CAMiLLe does not know about government.

43
Correspondences
  • The primary task of CAMiLLe is to construct
    correspondences, that is, mappings of strings of
    words into meanings (... based purely on the
    pairing of strings and meanings).
  • dog ? DOG(TYPEANIMAL)
  • see Robin ? SEE(THEMEROBIN)
  • see the big bird ? SEE(THEMEBIRD(ATTRIBBIG,RE
    FDEF))
  • see category ? SEE(THEMECATEGORY)

44
Rule formation
  • CAMiLLe incorporates the information provided by
    every sentence into a new rule or into an update
    of existing rules.
  • The relevant information consists of the linear
    ordering of words and other formatives in the
    string .
  • The meaning of each sentence is compared with all
    existing rules in terms of the meaning features
    mentioned in the rule.
  • CAMiLLe extracts those features of meaning that
    are possibly relevant to the correspondence.

45
CAMiLLe constructs...
  • word/meaning correspondences
  • string/meaning correspondences (idioms)
  • category/category correspondences (based on
    identical or very similar distribution)
  • category/category correspondences in strings
    (limited constructions or templates)

46
  • But to go beyond templates, CAMiLLe needs to
    generalize.
  • And to learn the grammar of English, CAMiLLe
    needs a more sophisticated understanding of what
    kinds of relations may hold across a string of
    words than it currently has.
  • We believe that this is an achievable goal.

47
So where do constraints/universals come from?
  • Competing formulations of the sound/meaning
    correspondences in a social network

48
The social network simulation
  • Agents
  • Social impact function
  • Parameter Interaction partners
  • Parameter Interaction distance
  • Knowledge of language
  • Features (in this case, 3)
  • and feature values (2, 4, 8, etc)
  • Noise (all other factors lumped together)

49
Assumptions about learning
  • Each learner interacts with a number of
    individuals at each time t
  • Each learner is influenced by the individuals
    that it interacts with dependent on their
    relative strength.
  • Majority wins
  • There is no review or evaluation by the learner
    of its own internal state

50
Display (start)
51
Language distribution (start)
The Tower of Babel
52
Strengths of individuals
53
Gaps
  • It is well-known that there are gaps in the
    possible languages, that is, languages that are
    logically possible but do not exist. Is this a
    deep fact about cognition (explained by UG), or
    not?
  • maybe, maybe not.

54
Hypothesis
  • Differential complexity of different ways of
    expressing a CS introduces a bias into the
    network against some alternatives
  • These will be disfavored in the network, perhaps
    even disappear.

55
Initial random distribution of feature values
56
Initial population of the eight languages
57
Distribution of languages and features after 150
steps
58
Language distribution after 150 steps
59
Some examples of what CAMiLLe does
60
Identifying and categorizing nouns(Input Nouns
1, Eve)
  • you xxx more cookies ? COOKIE(TYPEFOOD)
  • how about another graham cracker ?
    COOKIE(TYPEFOOD)
  • would that do just as well ?
  • here .
  • here you go .
  • you have another cookie right on the table .
    COOKIE(TYPEFOOD)
  • more juice ? JUICE(TYPEDRINK)
  • would you like more grape juice ?
    JUICE(TYPEDRINK)
  • where's your cup ? CUP(TYPEUTENSIL)
  • oh I took it .
  • I think that was Fraser . FRASER(TYPEPERSON)
  • I'm not sure .
  • what ?
  • are you saying Fraser ? FRASER(TYPEPERSON)
  • Mr Fraser ? FRASER(TYPEPERSON)
  • yes that's much better .
  • Mr Fraser ? FRASER(TYPEPERSON)
  • what is that ?
  • huh ?

61
Sample of noun correspondences identified by
CAMilLLe in Nouns 1
  • CHAIR ? chair
  • CHAIR(TYPEFURNITURE) ? chair
  • CLOTHES ? hat
  • DIAPER ? diaper
  • DIAPER(TYPECLOTHES) ? diaper
  • FRASER ? Fraser
  • FRASER(TYPEPERSON) ? Fraser
  • FURNITURE ? chair
  • HAT ? hat
  • HAT(TYPECLOTHES) ? hat
  • HEAD ? head
  • HEAD(TYPEBODYPART) ? head
  • JUICE ? juice
  • JUICE(TYPEDRINK) ? juice
  • PENCIL ? pencil
  • PENCIL(TYPEUTENSIL) ? pencil
  • PUDDING ? pudding
  • PUDDING(TYPEFOOD) ? pudding
  • STOOL ? stool

62
Nouns exemplified in Nouns 1
  • baby
  • book
  • bottle
  • box
  • chair
  • cheese
  • coffee
  • cookie
  • cup
  • diaper
  • duck
  • Eve
  • eye
  • Fraser
  • hat
  • head
  • juice
  • man
  • milk
  • mommy
  • paper
  • pencil
  • pudding
  • radio
  • shoe
  • fly
  • soldiers
  • stool
  • telephone
  • train
  • water

63
Nouns learned in Nouns 1
  • baby
  • book
  • bottle
  • box
  • chair
  • cheese
  • coffee
  • cookie
  • cup
  • diaper
  • duck
  • Eve
  • eye
  • Fraser
  • hat
  • head
  • juice
  • man
  • milk
  • mommy
  • paper
  • pencil
  • pudding
  • radio
  • shoe
  • fly
  • soldiers
  • stool
  • telephone
  • train
  • water

64
Why CAMiLLe thinks that telephone is a sentence
final element
  • well go and get your telephone .
    TELEPHONE(TYPETOY)
  • yes he gave you your telephone .
    TELEPHONE(TYPETOY)
  • yes that's the telephone . TELEPHONE(TYPETOY)
  • that was the telephone . TELEPHONE(TYPETOY)
  • it was Papa on the telephone .
    TELEPHONE(TYPETOY)
  • yes the telephone . TELEPHONE(TYPETOY)

65
Verb 2
1. a give b to c GIVE(AGENTA,THEMEB,RECIP
C) 2. does y give x to z GIVE(AGENTY,THEMEX
,RECIPZ) GIVE ? give to give2-gtto
givelt-2-gtto P1 GIVE ? 1.a 2.give 3.b
4.to 5.c 1.does 2.y 3.give 4.x 5.to
6.z P0.5 3. tell s to give r to q please
GIVE(AGENTS,THEMER,RECIPQ) 4. give j to e
now GIVE(THEMEJ,RECIPE) Same results,
stronger rules 5. c doesn't give k to a
NEG(GIVE(AGENTC,THEMEK,RECIPA)) 6. can q
give the w to s ? QU(GIVE(AGENTQ,THEMEW,RE
CIPS)) GIVE ? give to P1 GIVE ?
give to give2-gtto givelt-2-gtto P0.833333 (
And with richer input we get GIVE(RECIPE) ?
give to give2-gtto givelt-2-gtto e to1-gte
elt-1-gtto)
66
Verbal inflection
  • Input
  • Mary have s sleep en soundly
    SLEEP(AGENTMARY,ASPECTCOMPLETE,TIMENOW)
  • John be s really snore ing SNORE(AGENTJOHN,
    ASPECTPROGRESSIVE,TIMENOW)
  • John probably have s fall en
    FALL(EXPJOHN,ASPECTCOMPLETE,TIMENOW)
  • John might have s see en SEE(EXPJOHN,ASPEC
    TCOMPLETE,TIMENOW)
  • John suddenly see ed it SEE(EXPJOHN,PAST)
  • and then Mary fall ed FALL(THEMEMARY,PAST)

67
Output (ranked in order of strength)
  • JOHN MARY ?john mary
  • COMPLETE ? have en
  • NOW ? s
  • PAST ? ed
  • PROG ? be ing
  • DIE(ASPECTCOMPLETE) ? die en die1-gten
    dielt-1-gten
  • EAT(ASPECTCOMPLETE) ? 5.eat en eat1-gten
    eatlt-1-gten
  • FALL(ASPECTPROG) ? fall ing fall1-gting
    falllt-1-gting
  • FALL(ASPECTCOMPLETE) ? fall en fall1-gten
    falllt-1-gten
  • FIND ? find s findlt-1-gts
  • FIND(ASPECTCOMPLETE) ? have find have2-gtfind
    findlt-2-gthave s s1-gtfind findlt-1-gts en
    find1-gten findlt-1-gten
  • FIND(TIMENOW) ? find s findlt-1-gts
  • JUMP(ASPECTPROG) ? jump ing jump1-gting
    jumplt-1-gting
  • JUMP(ASPECTCOMPLETE) ? have jump have2-gtjump
    havelt-2-gtjump s s1-gtjump jumplt-1-gts en
    jump1-gten jumplt-1-gten
  • JUMP(TIMENOW) ? jump
  • JUMP(TIMEPAST) ? jump ed jumplt-1-gted
  • LOOK(ASPECTCOMPLETE) ? look en look1-gten
    looklt-1-gten
  • LOOK(TIMENOW) ? look
  • RUN(ASPECTCOMPLETE) ? run en run1-gten
    runlt-1-gten

68
  • SEE(TIMENOW) ? see s seelt-1-gts
  • SLEEP(ASPECTCOMPLETE) ? sleep en sleep1-gten
    sleeplt-1-gten
  • SLEEP(ASPECTCOMPLETE PROG ? sleep en
    ing sleep1-gten ing sleeplt-1-gten
    ing
  • SLEEP(ASPECTPROG) ? sleep ing sleep1-gting
    sleeplt-1-gting
  • SNORE(ASPECTPROG) ? snore ing snore1-gting
    snorelt-1-gting
  • SNORE(ASPECTCOMPLETE) ? snore en snore1-gten
    snorelt-1-gten
  • DIE LOOK(TIMENOW) ? die look en die
    look1-gten die looklt-1-gten
  • DIE EAT LOOK ? die eat look en die
    eat look 1-gten die eat look lt-1-gten
  • DIE FALL LOOK RUN SLEEP SNORE
    (ASPECTCOMPLETE) ?die fall look run
    sleep snore en die fall look run sleep
    snore 1-gten die fall look run sleep
    snore lt-1-gten

69
  • FALL SLEEP SNORE(ASPECTCOMPLETE PROG
    ? fall sleep snore en ing fall
    sleep snore1-gten ing
  • FALL JUMP SLEEP SNORE (ASPECTPROG) ?
    fall jump sleep snore ing fall jump
    sleep snore 1-gting
  • FALL RUN (TIMENOW) ? 5.fall run
  • FIND JUMP ? have find jump have2-gtfind
    jump s s1-gtfind jump en find jump
    1-gten
  • FIND JUMP(TIMENOW) ? have find jump
    have2-gtfind jump s1-gtfind jump find
    jump1-gten
  • FIND JUMP SLEEP (ASPECTCOMPLETE) ? have
    find jump sleep have2-gtfind jump
    sleep s1-gtfind jump sleep find jump
    sleep 1-gten
  • FIND SEE (TIMENOW) ? find see s
  • JUMP RUN (TIMEPAST) ? jump run ed
    jump run lt-1-gted

70
DP Structure
  • Input, DP3
  • See the man SEE(THEMEMAN(TYPEPERSON))
  • See the woman SEE(THEMEWOMAN(TYPEPERSON))
  • look at the baby LOOK(THEMEBABY(TYPEPERSON))
  • that 's a cat CAT(TYPEANIMAL)
  • and this is a dog DOG(TYPEANIMAL)
  • show me the horse SHOW(THEMEHORSE(TYPEANIMAL
    ))
  • what a tall man MAN(TYPEPERSON,ATTRTALL)
  • he is a nice man MAN(TYPEPERSON,ATTRNICE)
  • and this is a tall woman WOMAN(TYPEPERSON,ATT
    RTALL)
  • look at the little baby LOOK(THEMEBABY(TYPEP
    ERSON,ATTRLITTLE))
  • see the nice cat SEE(THEMECAT(TYPEANIMAL,AT
    TRNICE))
  • see the big dog SEE(THEMEDOG(TYPEANIMAL,ATT
    RBIG))
  • and the big horse HORSE(TYPEANIMAL,ATTRBIG)
  • that 's a little dog DOG(TYPEANIMAL,ATTRLITT
    LE)

71
Rule formed in DP3
  • BABY DOG HORSE
  • big little baby dog horsebiglittle1-
    gtbaby dog horse

72
Argument structure
  • 1. 7704 SEE ?see
  • 7. 392 SEE(EXPME) ? see . see1-gt
    2-gt. i1-gt 2-gt see
  • 9. 250 SEE(THEMEIT YOU ? see it
    you see1-gtit you
  • 10. 216 SEE(THEMEBECKY EVE ? see
    see1-gtbecky eve
  • Rules for see based on 115 sentences with see
    spoken to one actual child

73
Word order argument structure
  • Input
  • 18. I see mary SEE(EXPME,THEMEMARY(TYPEPE
    RSON))
  • 19. do you see john ? SEE(EXPYOU,THEMEJOHN(
    TYPEPERSON))
  • 20. I see john SEE(EXPME,THEMEJOHN(TYPEPE
    RSON))
  • 21. and I see a boy too SEE(EXPME,THEMEBOY(
    TYPEPERSON))
  • Rules
  • SEE(EXPME) ? i see i1-gtsee
  • SEE(THEMEJOHN MARY ? see john mary
    see1-gtjohn mary
  • SEE(THEMEBOY) ? see boy see2-gtboy

74
Discontinuous dependencies
  • Input
  • you are say-ing something SAY(AGENTYOU,THEME
    THING(REFINDEF))
  • you will say something SAY(AGENTYOU,THEMETH
    ING(REFINDEF))
  • you did say something SAY(AGENTYOU,THEMETHI
    NG(REFINDEF))
  • you are say-ing nonsense SAY(AGENTYOU,THEME
    NONSENSE)
  • you will say nonsense SAY(AGENTYOU,THEMENONS
    ENSE)
  • you did say nonsense SAY(AGENTYOU,THEMENONSE
    NSE)
  • what are you say-ing QU(SAY(AGENTYOU,THEME
    WHTHING))
  • what will you say QU(SAY(AGENTYOU,THEMEWH
    THING))
  • what did you say QU(SAY(AGENTYOU,THEMEWHT
    HING))
  • and similarly with do, eat
  • Rules
  • DO EAT SAY(THEMEWHTHING) ? 1.what 4.do
    eat say

75
Mock-Japanese wh-questions
  • Input
  • 400 sentences of the general form
  • this ACC do IMP . IMP(DO(THEMETHING(REFDEF
    )))
  • sing IMP . IMP(SING(AGENTPERSON))
  • sleep PRES Q . QU(SLEEP(AGENTPERSON))
  • what ACC do PAST Q . QU(DO(AGENTPERSON,THEM
    EWHTHING))
  • where TOP men NOM carrots ACC eat FUT Q .
    QU(EAT(AGENTMAN,THEMECARROT,PLACEWHPLACE))

76
Sample of rules formed
  • QU ? q .
  • SAY ? say X .
  • DO ? do X .
  • IMP ? imp .
  • WHTHING ? what acc X X q .
  • WOMAN ? woman1-gtnom
  • SAY ? say
  • EAT ? eat
  • IMP QU ? imp q .
  • DO ? do
  • QU(DO EAT SLEEP ? do eat sleep X q .
  • DO(THEMEWHTHING) ? what1-gtacc do2-gtq .
  • CARROT ? carrots1-gtacc
  • SAY(THEMEWHTHING) ? what1-gtacc q say
    say2-gtq
  • EAT ? eat
  • NONSENSE ? nonsense1-gtacc
  • DO SAY(THEMEWHTHING) ? what1-gtacc q do
    say2-gtq
  • DO EAT SAY ? .do eat say
  • EAT(AGENTCHILD MAN WOMAN) ? child men
    woman nom

77
Scrambling
  • hit bill-acc HIT(AGENTHIM,THEMEBILL)
  • (He) hits Bill.
  • bob-nom see SEE(EXPBOB)
  • Bob sees.
  • See jim-acc SEES(EXPHIM,THEMEJIM)
  • (He) sees Jim.
  • ...
  • CAMiLLe correctly determines that the EXP role
    of SEE is assigned to the (interpretation of the)
    DP marked -nom, that the THEME role of SEE is
    assigned to the (interpretation of the) DP marked
    -acc, and that the AGENT role of HIT is assigned
    to the (interpretation of the) DP marked -nom.
  • SEE(EXPARTHUR BOB) ? 1. arthur bob 2.nom
    3.see
  • SEE(THEMEARTHUR BOB) ? arthur bob acc
    arthur bob1-gtacc arthur boblt-1-gtacc
  • HIT(AGENTARTHUR BOB) ? 1.hit 2. arthur
    bob 3.nom

78
Inversion (English type)
  • Input
  • John JOHN(TYPEPERSON)
  • Bird BIRD(TYPEANIMAL)
  • John see s the bird SEE(EXPJOHN,THEMEBIRD)
  • do s John see a bird QU(SEE(EXPJOHN,THEMEB
    IRD))
  • the bird see s John SEE(EXPBIRD,THEMEJOHN)
  • do s the bird see John QU(SEE(EXPBIRD,THEME
    JOHN))
  • a bird see s John SEE(EXPBIRD,THEMEJOHN)
  • ...
  • Rules
  • QU ? 1.do 2.s

79
Inversion (non-English type)
  • Input
  • Sandy eat s the cake EAT(AGENTSANDY,THEMECA
    KE)
  • eat s Sandy the cake QU(EAT(AGENTSANDY,THEM
    ECAKE))
  • Sandy come s COME(AGENTSANDY)
  • come s Sandy QU(COME(AGENTSANDY))
  • Sandy go s GO(AGENTSANDY)
  • go s Sandy QU(GO(AGENTSANDY))
  • ...
  • Rules
  • QU ? 2.s

80
Imperatives
  • Sample Input
  • eat IMP(EAT(AGENTYOU))
  • eat the cereal IMP(EAT(AGENTYOU,THEMECEREA
    L))
  • drink IMP(DRINK(AGENTYOU))
  • drink the milk IMP(DRINK(AGENTYOU,THEMEMIL
    K))
  • Rules
  • DRINK EAT(AGENTYOU) ? 1.drink eat
  • HEAR SEE(EXPYOU) ? 1.hear see
  • IMP(DRINK EAT HEAR LOOK SEE) ? 1.drink
    eat hear look see

81
Templates
  • These are all templates they are not
    correspondence rules that take into account
    grammatical structure, and hence cannot be fully
    general.
  • They are good for local constructions (like
    inversion), but not for those that hold over
    unbounded strings (like wh-questions and many
    others).

82
  • CAMiLLe needs to know about grammatical structure
    in order to find grammatical structure in the
    input.
  • CAMiLLe needs to know about relations that hold
    across syntactic structures in order to find such
    relations in the input.
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