NTL – Converging Constraints
• Basic concepts and words derive their meaning
from embodied experience.
• Abstract and theoretical concepts derive their
meaning from metaphorical maps to more basic
embodied concepts.
• Structured Connectionist Models can capture
both of these processes nicely.
• Grammar extends this by Constructions:
pairings of form with embodied meaning.
Simulation-based language understanding
“Harry walked to the cafe.”
Utterance
Constructions
Analysis Process
General
Knowledge
Belief State
Schema
walk
Trajector
Harry
Cafe
Goal
cafe
Simulation
Specification
Simulation
The ICSI/Berkeley
Neural Theory of Language Project
Background: Primate Motor Control
• Relevant requirements (Stromberg, Latash, Kandel,
Arbib, Jeannerod, Rizzolatti)
– Should model coordinated, distributed, parameterized
control programs required for motor action and
perception.
– Should be an active structure.
– Should be able to model concurrent actions and
interrupts.
• Model
– The NTL project has developed a computational model
based on that satisfies these requirements (x- schemas).
– Details, papers, etc. can be obtained on the web at
http://www.icsi.berkeley.edu/NTL
Active representations
• Representation based on stochastic Petri nets captures
dynamic, parameterized nature of actions
• Many inferences about actions derive from what we know
about executing them
• Generative model: action, planning, recognition, language.
walker at goal
energy
walker=Harry
goal=home
Walking:
bound to a specific walker with a
direction or goal
consumes resources (e.g., energy)
may have termination condition
(e.g., walker at goal)
ongoing, iterative action
Active Motion Model
Evolving Responses
of Competing
Models over Time.
Nigel Goddard
1989
Language Development in
Children
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0-3 mo: prefers sounds in native language
3-6 mo: imitation of vowel sounds only
6-8 mo: babbling in consonant-vowel segments
8-10 mo: word comprehension, starts to lose sensitivity to
consonants outside native language
12-13 mo: word production (naming)
16-20 mo: word combinations, relational words (verbs,
adj.)
24-36 mo: grammaticization, inflectional morphology
3 years – adulthood: vocab. growth, sentence-level
grammar for discourse purposes
Learning Spatial Relation Words
Terry Regier
A model of children learning spatial relations.
Assumes child hears one word label of scene.
Program learns well enough to label novel scenes
correctly.
Extended to simple motion scenarios, like INTO.
System works across languages.
Mechanisms are neurally plausible.
Learning
System
dynamic relations
(e.g. into)
structured connectionist
network (based on
visual system)
We’ll look at the
details next lecture
Limitations
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Scale
Uniqueness/Plausibility
Grammar
Abstract Concepts
Inference
Representation
Biological Realism
Constrained Best Fit in Nature
inanimate
physics
chemistry
biology
vision
language
animate
lowest energy
state
molecular
minima
fitness, MEU
neuroeconomics
threats,
friends
errors,
NTL
Learning Verb Meanings
David Bailey
A model of children learning their first verbs.
Assumes parent labels child’s actions.
Child knows parameters of action, associates with
word
Program learns well enough to:
1) Label novel actions correctly
2) Obey commands using new words (simulation)
System works across languages
Mechanisms are neurally plausible.
Motor Control (X-schema) for SLIDE
Parameters for the SLIDE X-schema
Feature Structures for PUSH
System Overview
Learning Two Senses of PUSH
Model merging based on Bayesian MDL
Training Results
David Bailey
English
• 165 Training Examples, 18 verbs
• Learns optimal number of word senses (21)
• 32 Test examples : 78% recognition, 81% action
• All mistakes were close lift ~ yank, etc.
• Learned some particle CXN,e.g., pull up
Farsi
• With identical settings, learned senses not in
English
Constrained Best Fit in Nature
inanimate
physics
chemistry
biology
vision
language
animate
lowest energy
state
molecular
minima
fitness, MEU
neuroeconomics
threats,
friends
errors,
NTL
Compositionality
• Traditional
Context-free composition of logical forms
• Contemporary
Constructional composition of conceptual
frames
Formal Cognitive Linguistics
Embodied Construction
Grammar
(Bergen, Chang & Paskin 2000)
• Assumptions from Construction Grammar
– Constructions are form-meaning pairs
(Lakoff 1987, Goldberg 1995)
– Constructions vary in degree of specificity and level of
description (morphological, lexical, phrasal, clausal)
• Constructions evoke and bind semantic schemas
• Additional influences
– Cognitive Grammar (Langacker 1987)
– Frame Semantics (Fillmore 1982)
– Structured Connectionism (Feldman 1987)
Traditional Levels of Analysis
Pragmatics
Semantics
Syntax
Morphology
Phonetics
“Harry walked into the cafe.”
Pragmatics
Semantics
Utterance
Syntax
Morphology
Phonetics
Language understanding: analysis &
simulation
construction WALKED in context c
constituents:
form f of type [wakt]
meaning walking construed as Walk-Action
semantic constraints:
walking.time before c.speech-time
walking.aspect = encapsulated
designates walking
“Harry walked into the cafe.”
Utterance
Analysis Process
Constructions
General
Knowledge
Simulation
Specification
Belief State
CAFE
Simulation
Simulation specification
A simulation specification consists of:
•semantic schemas evoked by constructions
•bindings between schemas (labeled by the constructions that enforce them)
Conclusion
• Language acquisition and use is a hallmark of being human
– Language seems to rely on fine-grained aspects of embodied (sensory-motor and
social cognition) primitives and brain-like computation (massively parallel,
distributed, spreading activation, temporal binding).
– Understanding requires imaginative simulation!
– We have built a pilot system that demonstrates the use of motor control
representations in grounding the language of abstract actions and policies.
• Sensory-Motor imagination and simulation is crucial in interpretation!
• Ongoing Work.
– Formalize and use a compositional set of embodied conceptual primitives and
grammatical constructions.
– Perform both behavioral and fMRI imaging experiments to test the predictions
of the simulation hypothesis.
– Further refine and ground the model in details of neural anatomy and functional
architecture (basal-thalamic-cortical loops).