The ICSI/BerkeleyNeural Theory of Language Project
• Principal investigators Jerome Feldman (UCB,ICSI) George Lakoff (UCB Ling) Srini Narayanan (UCB,ICSI) Lokendra Shastri (now India)
• Affiliated faculty
Chuck Fillmore (ICSI) Eve Sweetser (UCB Ling) Rich Ivry (UCB Psych) Lisa Aziz-Zadeh (USC)
Graduate Students Leon Barrett (CS) *Johno Bryant (CS) *Nancy Chang (CS) Ellen Dodge (Ling) Michael Ellsworth (Ling) Joshua Marker (Ling) *Eva Mok (CS) Shweta Narayan (Ling) *Steve Sinha (CS)
Alumni Terry Regier (UCB Ling) David Bailey (Google) Andreas Stolcke (ICSI, SRI) Dan Jurafsky (Stanford Ling) Olya Gurevich (Powerset) Benjamin Bergen (U. Hawaii Ling) Carter Wendelken (UCB) Srini Narayanan (ICSI, UCB) Gloria Yang (UTD)
Unified Cognitive Science
Neurobiology
Psychology
Computer Science
Linguistics
Philosophy
Social Sciences
Experience
Take all the Findings and Constraints Seriously
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
Brains ~ Computers
• 1000 operations/sec• 100,000,000,000 units• 10,000 connections/• graded, stochastic• embodied• fault tolerant• evolves• learns
• 1,000,000,000 ops/sec• 1-100 processors• ~ 4 connections• binary, deterministic• abstract, disembodied• crashes frequently• explicitly designed • is programmed
Fast Brain ~ Slow Neurons
Mental Connections are Active Neural Connections
There is No Erasing in the Brain
Constraints on Connectionist Models
100 Step Rule
Human reaction times ~ 100 milliseconds
Neural signaling time ~ 1 millisecond
Simple messages between neurons
Long connections are rare
No new connections during learning
Developmentally plausible
Connectionist Models in Cognitive Science
Structured PDP
Neural Conceptual Existence Data Fitting
Hybrid
Fast Mapping Skill Learning
Not discussed in meeting
Triangle nodes and McCullough-Pitts Neurons?
B C
A
A B C
Representing concepts using triangle nodes
Functionalism
In fact, the belief that neurophysiology is even relevant to the functioning of the mind is just a hypothesis. Who knows if we’re looking at the right aspects of the brain at all. Maybe there are other aspects of the brain that nobody has even dreamt of looking at yet. That’s often happened in the history of science. When people say that the mental is just the neurophysiological at a higher level, they’re being radically unscientific. We know a lot about the mental from a scientific point of view. We have explanatory theories that account for a lot of things. The belief that neurophysiology is implicated in these things could be true, but we have very little evidence for it. So, it’s just a kind of hope; look around and you see neurons: maybe they’re implicated.
Noam Chomsky 1993, p.85
Embodiment
Of all of these fields, the learning of languages would be the most impressive, since it is the most human of these activities. This field, however, seems to depend rather too much on the sense organs and locomotion to be feasible.
Alan Turing (Intelligent Machines,1948) Continuity Principle of the American Pragmatists
The ICSI/BerkeleyNeural Theory of Language Project
Learning early constructions (Chang, Mok)
ECG
Ideas from Cognitive Linguistics
• Embodied Semantics (Lakoff, Johnson, Sweetser, Talmy
• Radial categories (Rosch 1973, 1978; Lakoff 1985)
– mother: birth / adoptive / surrogate / genetic, …
• Profiling (Langacker 1989, 1991; cf. Fillmore XX)
– hypotenuse, buy/sell (Commercial Event frame)
• Metaphor and metonymy (Lakoff & Johnson 1980, …)
– ARGUMENT IS WAR, MORE IS UP– The ham sandwich wants his check.
• Mental spaces (Fauconnier 1994)
– The girl with blue eyes in the painting really has green eyes.
• Conceptual blending (Fauconnier & Turner 2002, inter alia)
– workaholic, information highway, fake guns– “Does the name Pavlov ring a bell?” (from a talk on ‘dognition’!)
Simulation-based language understanding
“Harry walked to the cafe.”
Schema Trajector Goalwalk Harry cafe
Analysis Process
Simulation Specification
Utterance
SimulationCafe
Constructions
General Knowledge
Belief State
Psycholinguistic evidence• Embodied language impairs action/perception
– Sentences with visual components to their meaning can interfere with performance of visual tasks
(Richardson et al. 2003)
– Sentences describing motion can interfere with performance of incompatible motor actions
(Glenberg and Kashak 2002)
– Sentences describing incompatible visual imagery impedes decision task (Zwaan et al. 2002)
• Simulation effects from fictive motion sentences– Fictive motion sentences describing paths that require
longer time, span a greater distance, or involve more obstacles impede decision task (Matlock 2000, Matlock et al. 2003)
Neural evidence: Mirror neurons• Gallese et al. (1996) found “mirror” neurons
in the monkey motor cortex, activated when– an action was carried out– the same action (or a similar one) was seen.
• Mirror neuron circuits found in humans (Porro et al. 1996)
• Mirror neurons activated when someone:– imagines an action being carried out (Wheeler et al.
2000)
– watches an action being carried out (with or without object) (Buccino et al. 2000)
Active representations• Many inferences about actions derive from what we know
about executing them• Representation based on stochastic Petri nets captures
dynamic, parameterized nature of actions• Used for acting, recognition, planning, and language
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
walker=Harry
goal=home
energy
walker at goal
Learning Verb MeaningsDavid Bailey
A model of children learning their first verbs.Assumes parent labels child’s actions.Child knows parameters of action, associates with wordProgram learns well enough to: 1) Label novel actions correctly 2) Obey commands using new words (simulation)System works across languagesMechanisms are neurally plausible.
System Overview
Learning Two Senses of PUSH
Model merging based on Bayesian MDL
NTL Manifesto
• Basic Concepts are Grounded in Experience– Sensory, Motor, Emotional, Social,
• Abstract and Technical Concepts map by Metaphor to more Basic Concepts
• Neural Computation models all levels
Simulation based Language Understanding
Constructions
Simulation
Utterance Discourse & Situational Context
Semantic Specification:
image schemas, frames, action schemas
Analyzer:
incremental,competition-based, psycholinguistically
plausible
“Harry walked into the cafe.”
Phonology
Semantics
Pragmatics
Morphology
Syntax
Phonetics
“Harry walked into the cafe.”
Phonology
Semantics
Pragmatics
Morphology
Syntax
Phonetics
UTTERANCE
Embodied Construction Grammar• Embodied representations
– active perceptual and motor schemas(image schemas, x-schemas, frames, etc.)
– situational and discourse context
• Construction Grammar– Linguistic units relate form and
meaning/function.– Both constituency and (lexical) dependencies
allowed.
• Constraint-based– based on feature unification (as in LFG, HPSG)– Diverse factors can flexibly interact.
Embodiment and Grammar Learning
Paradigm problem for Nature vs. Nurture
The poverty of the stimulus
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.
Embodied Construction GrammarECG
(Formalizing Cognitive Linguisitcs)
1. Linguistic Analysis
2. Computational Implementationa. Test Grammars
b. Applied Projects – Question Answering
3. Map to Connectionist Models, Brain
4. Models of Grammar Acquisition