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