Embodied Construction Grammar ECG (Formalizing Cognitive Linguistics )
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Transcript of Embodied Construction Grammar ECG (Formalizing Cognitive Linguistics )
Embodied Construction GrammarECG
(Formalizing Cognitive Linguistics)
1. Community Grammar and Core Concepts2. Deep Grammatical Analysis3. Computational Implementation
a. Test Grammars b. Applied Projects – Question Answering
4. Map to Connectionist Models, Brain5. Models of Grammar Acquisition
Simulation specification
The analysis process produces a simulation specification that
•includes image-schematic, motor control and conceptual structures
•provides parameters for a mental simulation
Summary: ECG• Linguistic constructions are tied to a model of
simulated action and perception• Embedded in a theory of language processing
– Constrains theory to be usable– Basis for models of grammar learning
• Precise, computationally usable formalism– Practical computational applications, like MT and NLU– Testing of functionality, e.g. language learning
• A shared theory and formalism for different cognitive mechanisms– Constructions, metaphor, mental spaces, etc.
• Reduction to Connectionist and Neural levels
physics lowest energy state
chemistry molecular fit
biology fitness, MEU Neuroeconomics
vision threats, friends
language errors, NTL
Constrained Best Fit in Natureinanimate animate
society, politicsframing, compromise
Competition-based analyzer• An analysis is made up of:
– A constructional tree– A semantic specification– A set of resolutions
Bill gave Mary the book
MaryBill
Ref-Exp Ref-Exp Ref-ExpGive
A-GIVE-B-Xsubj v obj1 obj2
book01@Man @WomanGive-Action @Book
giverrecipient
theme
Johno Bryant
Combined score determines best-fit
• Syntactic Fit:– Constituency relations– Combine with preferences on non-local elements– Conditioned on syntactic context
• Antecedent Fit:– Ability to find referents in the context– Conditioned on syntax match, feature agreement
• Semantic Fit:– Semantic bindings for frame roles– Frame roles’ fillers are scored
0Eve1walked2into3the4house5
Constructs--------------NPVP[0] (0,5)Eve[3] (0,1)ActiveSelfMotionPath
[2] (1,5)WalkedVerb[57] (1,2)SpatialPP[56] (2,5)Into[174] (2,3)DetNoun[173] (3,5)The[204] (3,4)House[205] (4,5)
Schema Instances-------------------
SelfMotionPathEvent[1]HouseSchema[66]WalkAction[60]Person[4]SPG[58]RD[177] ~ houseRD[5]~ Eve
Unification chains and their fillersSelfMotionPathEvent[1].mover
SPG[58].trajectorWalkAction[60].walkerRD[5].resolved-refRD[5].category
Filler: Person4 SpatialPP[56].mInto[174].mSelfMotionPathEvent[1].spg
Filler: SPG58
SelfMotionPathEvent[1] .landmarkHouse[205].mRD[177].categorySPG[58].landmark
Filler:HouseSchema66 WalkedVerb[57].mWalkAction[60].routineWalkAction[60].gaitSelfMotionPathEvent[1] .motion
Filler:WalkAction60
• Mother (I) give you this (a toy).
CHILDES Beijing Corpus (Tardiff, 1993; Tardiff, 1996)
ma1+ma
gei3 ni3 zhei4+
gemother give 2PS this+CLS
• You give auntie [the peach].
• Oh (go on)! You give [auntie] [that].
Productive Argument Omission (Mandarin)Johno Bryant & Eva Mok
1
2
3
ni3 gei3 yi2
2PS give auntie
ao ni3 gei3 ya
EMP 2PS give EMP4 gei
3 give
• [I] give [you] [some peach].
Arguments are omitted with different probabilities
All args omitted: 30.6% No args omitted: 6.1%
% elided (98 total utterances)
Giver
Recipient
Theme
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%60.00%
70.00%
80.00%
90.00%
100.00%
Analyzing ni3 gei3 yi2 (You give auntie)
• Syntactic Fit: – P(Theme omitted | ditransitive cxn) = 0.65– P(Recipient omitted | ditransitive cxn) = 0.42
Two of the competing analyses:
ni3 gei3 yi2 omitted↓ ↓ ↓ ↓
Giver Transfer Recipient Theme
ni3 gei3 omitted yi2↓ ↓ ↓ ↓
Giver Transfer Recipient Theme
(1-0.78)*(1-0.42)*0.65 = 0.08 (1-0.78)*(1-0.65)*0.42 = 0.03
Using frame and lexical information to restrict type of reference
Lexical Unit gei3Giver (DNI)Recipient (DNI)Theme (DNI)
The Transfer FrameGiverRecipientTheme
MannerMeansPlace
PurposeReasonTime
Can the omitted argument be recovered from context?
• Antecedent Fit:ni3 gei3 yi2 omitted↓ ↓ ↓ ↓
Giver Transfer Recipient Theme
ni3 gei3 omitted yi2↓ ↓ ↓ ↓
Giver Transfer Recipient Theme
Discourse & Situational Context
child motherpeach auntietable
?
How good of a theme is a peach? How about an aunt?
The Transfer FrameGiver (usually animate)Recipient (usually animate)Theme (usually inanimate)
ni3 gei3 yi2 omitted↓ ↓ ↓ ↓
Giver Transfer Recipient Theme
ni3 gei3 omitted yi2↓ ↓ ↓ ↓
Giver Transfer Recipient Theme
Semantic Fit:ni3 gei3 yi2 omitted↓ ↓ ↓ ↓
Giver Transfer Recipient Theme
The argument omission patterns shown earlier
can be covered with just ONE construction
• Each construction is annotated with probabilities of omission • Language-specific default probability can be set
Subj Verb Obj1 Obj2↓ ↓ ↓ ↓
Giver Transfer Recipient Theme
0.78 0.42 0.65P(omitted|cxn):
% elided (98 total utterances)
Giver
Recipient
Theme
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
Leverage process to simplify representation
• The processing model is complementary to the theory of grammar
• By using a competition-based analysis process, we can:– Find the best-fit analysis with respect to
constituency structure, context, and semantics
– Eliminate the need to enumerate allowable patterns of argument omission in grammar
• This is currently being applied in models of language understanding and grammar learning.
Modeling context for language understanding and learning• Linguistic structure reflects experiential structure
– Discourse participants and entities – Embodied schemas:
• action, perception, emotion, attention, perspective
– Semantic and pragmatic relations: • spatial, social, ontological, causal
• ‘Contextual bootstrapping’ for grammar learning
The context model tracks accessible entities, events, and utterances
Discourse & Situational
Context
Discourse01participants: Eve , Motherobjects: Hands, ...discourse-history: DS01situational-history: Wash-Action
Discourse:
Each of the items in the context model has rich internal structure
Situational History: Discourse History:
Participants: Objects:Discourse:
Wash-Actionwasher: Evewashee: Hands
DS01speaker: Motheraddressee: Eveattentional-focus: Handscontent: {"are they clean yet?"}speech-act: question
Evecategory: childgender: femalename: Eveage: 2
Mothercategory: parentgender: femalename: Eveage: 33
Handscategory: BodyPartpart-of: Evenumber: pluralaccessibility: accessible
Analysis produces a semantic specification
Linguistic Knowledge
UtteranceDiscourse & Situational
Context
Semantic Specification
World Knowledge
Analysis“You
washed them”
WASH-ACTIONwasher: Evewashee: Hands
How Can Children Be So Good At Learning Language?
• Gold’s Theorem:No superfinite class of language is identifiable in the limit from positive data only
• Principles & ParametersBabies are born as blank slates but acquire language quickly (with noisy input and little correction) → Language must be innate:
Universal Grammar + parameter settingBut babies aren’t born as blank slates!
And they do not learn language in a vacuum!
Key ideas for a NT of language acquisitionNancy Chang and Eva Mok
• Embodied Construction Grammar
• Opulence of the Substrate– Prelinguistic children already have rich sensorimotor
representations and sophisticated social knowledge
• Basic Scenes – Simple clause constructions are associated directly with
scenes basic to human experience(Goldberg 1995, Slobin 1985)
• Verb Island Hypothesis – Children learn their earliest constructions
(arguments, syntactic marking) on a verb-specific basis(Verb Island Hypothesis, Tomasello 1992)
Embodiment and Grammar Learning
Paradigm problem for Nature vs. Nurture
The poverty of the stimulus
The opulence of the substrate
Intricate interplay of genetic and environmental, including social, factors.
Two perspectives on grammar learning
Computational models
• Grammatical induction– language identification– context-free grammars,
unification grammars– statistical NLP (parsing,
etc.)• Word learning models
– semantic representations• logical forms• discrete representations• continuous
representations– statistical models
Developmental evidence
• Prior knowledge– primitive concepts– event-based knowledge– social cognition– lexical items
• Data-driven learning– basic scenes– lexically specific patterns– usage-based learning
Key assumptions for language acquisition
• Significant prior conceptual/embodied knowledge– rich sensorimotor/social substrate
• Incremental learning based on experience– Lexically specific constructions are learned
first.• Language learning tied to language
use– Acquisition interacts with comprehension,
production; reflects communication and experience in world.
– Statistical properties of data affect learning
Context
Eve
washer
Wash-Action
Hands
washee
Discourse Segment
addressee
attentional-focus
Analysis draws on constructions and context
before
before
MeaningForm
you Addressee
washer
Wash-Actionwashed
washee
ContextElementthem
Learning updates linguistic knowledge based on input utterances
LearningDiscourse & Situational
Context Linguistic Knowledge
AnalysisUtterance
PartialSemSpec
World Knowledge
Context
Eve
washer
Wash-Action
Hands
washee
Discourse Segment
addressee
attentional-focus
Context aids understanding: Incomplete grammars yield partial SemSpec
MeaningForm
you Addressee
washer
Wash-Actionwashed
washee
ContextElementthem
Context
Eve
washer
Wash-Action
Hands
washee
Discourse Segment
addressee
attentional-focus
Context bootstraps learning: new construction maps form to meaning
MeaningForm
you Addressee
Wash-Actionwashed
ContextElementthem
before
before washer
washee
Context bootstraps learning: new construction maps form to meaning
MeaningForm
you Addressee
Wash-Actionwashed
ContextElementthem
before
before washer
washee
YOU-WASHED-THEM constituents:
YOU, WASHED, THEMform:
YOU before WASHED WASHED before THEM
meaning: WASH-ACTIONwasher: addresseewashee: ContextElement
Grammar learning: suggesting new CxNs and reorganizing existing ones
reinforcement
reorganize• merge• join• split
Linguistic Knowledge
Discourse & Situational
Context
AnalysisUtterance
PartialSemSpec
World Knowledge
hypothesize• map form to
meaning• learn contextual
constraints
Challenge: How far up to generalize
• Eat rice• Eat apple• Eat watermelon
• Want rice• Want apple• Want chair
Inanimate Object
ManipulableObjects
Unmovable Objects
Food Furniture
Fruit Savory Chair Sofa
apple watermelon
rice
Challenge: Omissible constituents
• In Mandarin, almost anything available in context can be omitted – and often is in child-directed speech.
• Intuition:• Same context, two expressions that differ
by one constituent a general construction with the constituent being omissible
• May require verbatim memory traces of utterances + “relevant” context
When does the learning stop?
• Most likely grammar given utterances and context
• The grammar prior includes a preference for the “kind” of grammar
• In practice, take the log and minimize cost Minimum Description Length (MDL)
)(),|(argmax
),|(argmaxˆ
GPZGUP
ZUGPG
G
G
Bayesian Learning FrameworkSchemas +
Constructions
SemSpec
Analysis + Resolution
Context Fitting
reorganize
hypothesize
reinforcement
Intuition for MDL• S -> Give me NP• NP -> the book• NP -> a book
• S -> Give me NP• NP -> DET book• DET -> the• DET -> a
39
Suppose that the prior is inversely proportional to the size of the grammar (e.g. number of rules)
It’s not worthwhile to make this generalization
Intuition for MDL• S -> Give me NP• NP -> the book• NP -> a book• NP -> the pen• NP -> a pen• NP -> the pencil• NP -> a pencil• NP -> the marker• NP -> a marker
• S -> Give me NP• NP -> DET N• DET -> the• DET -> a• N -> book• N -> pen• N -> pencil• N -> marker
Usage-based learning: comprehension and production
reinforcement(usage)
reinformcent(correction)
reinforcement(usage)
hypothesize constructions& reorganize
reinforcement(correction)
constructicon
world knowledge
discourse & situational context
simulation
analysis
utterance
analyze &
resolve
utterance
response
comm. intent
generate