Language Understanding and Unified Cognitive Science

Jerome FeldmanInternational Computer Science Institute

U. California at BerkeleyBerkeley, CA

jfeldman@icsi.berkeley.edu

Unified Cognitive Science

Neurobiology

Psychology

Computer Science

Linguistics

Philosophy

Social Sciences

Experience

Take all the Findings and Constraints Seriously

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

LecturesI. Overview2. Simulation Semantics3. ECG and Best-fit Analysis4. Compositionality5. Simulation, Counterfactuals, and Inference

Constructions

Simulation

Utterance Discourse & Situational Context

Semantic Specification:

image schemas, bindings, action schemas

Analyzer:

incremental,competition-based,

psychologically plausible

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)

The Mirror System

The mirror system, like the motor system, is somatotopically organized.

Foot actions Hand actions Mouth actions

Buccino et al., 2001

humans watching videos of actions without objects

humans watching same actions with objects

mouthhandfoot

Fast Brain ~ Slow Neurons

Mental Connections are Active Neural Connections

There is No Erasing in the Brain

Movement vs. ActionsPulvermueller Lab

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

The ICSI/BerkeleyNeural Theory of Language Project

Learning early constructions (Chang, Mok)

ECG

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

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

The ICSI/BerkeleyNeural Theory of Language Project

Learning early constructions (Chang, Mok)

ECG

The Binding Problem

Massively Parallel Brain

Unitary Conscious Experience

Many Variations and Proposals

Our focus: The Variable Binding Problem

SHRUTI• SHRUTI does inference

by connections between simple computation nodes

• Nodes are small groups of neurons

• Nodes firing in sync reference the same object

Proposed Alternative Solution

• Indirect references– Pass short signatures, “fluents”

• Functionally similar to SHRUTI's time slices

– Central “binder” maps fluents to objects• In SHRUTI, the objects fired in that time slice

– Connections need to be more complicated than in SHRUTI

• Fluents are passed through at least 3 bits• But temporal synchrony is not required

LecturesI. Overview2. Simulation Semantics3. ECG and Best-fit Analysis4. Compositionality5. Simulation, Counterfactuals, and Inference

Constructions

Simulation

Utterance Discourse & Situational Context

Semantic Specification:

image schemas, bindings, action schemas

Analyzer:

incremental,competition-based,

psychologically plausible

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’!)

Image schemas

• Trajector / Landmark (asymmetric)– The bike is near the house – ? The house is near the bike

• Boundary / Bounded Region – a bounded region has a closed boundary

• Topological Relations– Separation, Contact, Overlap, Inclusion, Surround

• Orientation– Vertical (up/down), Horizontal (left/right, front/back)– Absolute (E, S, W, N)

LMTR

bounded region

boundary

Schema FormalismSCHEMA <name>

SUBCASE OF <schema>

EVOKES <schema> AS <local name>

ROLES < self role name>: <role restriction>

< self role name> <-> <role name>

CONSTRAINTS <role name> <- <value>

<role name> <-> <role name>

A Simple Example

 

SCHEMA hypotenuse

SUBCASE OF line-segment

EVOKES right-triangle AS rt

ROLES Comment inherited from line-segment

CONSTRAINTS

SELF <-> rt.long-side

Language understanding: analysis & simulation

“Harry walked into the cafe.”

Analysis Process

SemanticSpecification

Utterance

Constructions

General Knowledge

Belief State

CAFE Simulation

construction WALKEDform

selff.phon [wakt]meaning : Walk-Action constraints

selfm.time before Context.speech-time selfm..aspect encapsulated

Semantic specification

The analysis process produces a semantic specification that

•includes image-schematic, motor control and conceptual structures

•provides parameters for a mental simulation

Task: Interpret simple discourse fragments/ blurbs

France fell into recession. Pulled out by Germany

US Economy on the verge of falling back into recession after moving forward on an anemic recovery.

Indian Government stumbling in implementing Liberalization plan.

Moving forward on all fronts, we are going to be ongoing and relentless as we tighten the net of justice.

The Government is taking bold new steps. We are loosening the stranglehold on business, slashing tariffs and removing obstacles to international trade.

Results• Model was implemented and tested on discourse fragments from a

database of 50 newspaper stories in international economics from standard sources such as WSJ, NYT, and the Economist.

• Results show that motion terms are often the most effective method to provide the following types of information about abstract plans and actions.– Information about uncertain events and dynamic changes in goals

and resources. (sluggish, fall, off-track, no steam)– Information about evaluations of policies and economic actors and

communicative intent (strangle-hold, bleed).– Communicating complex, context-sensitive and dynamic

economic scenarios (stumble, slide, slippery slope).– Commincating complex event structure and aspectual information

(on the verge of, sidestep, giant leap, small steps, ready, set out, back on track).

• ALL THESE BINDINGS RESULT FROM REFLEX, AUTOMATIC INFERENCES PROVIDED BY X-SCHEMA BASED INFERENCES.

Embodied Construction Grammar

• Embodied representations– active perceptual and motor schemas

– situational and discourse context

• Construction Grammar– Linguistic units relate form and meaning/function.

– Both constituency and (lexical) dependencies allowed.

• Constraint-based (Unification)– based on feature structures (as in HPSG)

– Diverse factors can flexibly interact.

Embodied Construction GrammarECG

(Formalizing Cognitive Linguistics)

1. Community Grammar and Core Concepts

2. Deep Grammatical Analysis

3. Computational Implementationa. Test Grammars

b. Applied Projects – Question Answering

4. Map to Connectionist Models, Brain

5. Models of Grammar Acquisition

Construction BITE1 subcase of Verb form: bite meaning: ForceApplication constraints: Effector ← teeth Routine ← bite // close mouth 

Verb Constructions

schema ForceApplication subcase of MotorControl evokes ForceTransfer as FT roles Actor ↔ FT.Supplier ↔ Protagonist Acted Upon ↔ FT.Recipient Effector Routine Effort ↔ FT.Force.amount

Semantic SpecificationHe bit the apple

EventDescriptor eventtype ProfiledProcess ProfiledParticipant

CauseEffect causer affected

ForceApplication actor actedupon routine bite effector teeth

RD55category

Person

Apple

RD27category

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

physics lowest energy state

chemistry molecular

minima

biology fitness, MEU

Neuroeconomics

vision threats,

friends

language errors,

NTL

Constrained Best Fit in Natureinanimate animate

Two perspectives on language learning

Computational models• Grammatical induction

– language identification– context-free grammars,

unification-based grammars– statistical NLP

• Word learning models– semantic representations

• logical forms• discrete representations• continuous representations

– statistical models

Developmental evidence

• Prior knowledge– concepts– event-based knowledge– social cognition– lexical items

• Data-driven learning– basic scenes– lexically specific patterns– usage-based learning

Language Acquisition• Opulence of the substrate

– Prelinguistic children already have rich sensorimotor representations and sophisticated social knowledge

– intention inference, reference resolution– language-specific event conceptualizations

(Bloom 2000, Tomasello 1995, Bowerman & Choi, Slobin, et al.)

• Children are sensitive to statistical information– Phonological transitional probabilities– Most frequent items in adult input learned earliest

(Saffran et al. 1998, Tomasello 2000)

Experiment: learning verb islands

• Given: initial lexicon and ontology

• Data: child-directed language annotated with contextual information

Form:

Participants : Mother, Naomi, Ball

Scene :

Discourse :

text : throw the ballintonation : falling

Throwthrower : Naomithrowee : Ball

speaker :Motheraddressee Naomispeech act : imperativeactivity : playjoint attention : Ball

• Question:– Can the proposed construction learning model

acquire English item-based motion constructions? (Tomasello 1992)

The intuition behind learning a new form-meaning pairing from context

construction Put

construction Coat

construction Here

Put-Actionput-agentput-themelocation

Coat

Sofa

before

before

The learner learns a new lexically-specific construction from the form-meaning pair

construction Put-Coat-Here

constituentsv: Puto: Coatp: Here

formvf before of before pf

meaning: Caused-Motion-Sceneselfm.means vm

selfm.mover om

selfm.path pm

Experiment: learning verb islands

Subset of the CHILDES database of parent-child interactions (MacWhinney 1991; Slobin )

• coded by developmental psychologists for– form: particles, deictics, pronouns, locative phrases, etc.

– meaning: temporality, person, pragmatic function,type of motion (self-movement vs. caused movement; animate being vs. inanimate object, etc.)

• crosslinguistic (English, French, Italian, Spanish)– English motion utterances: 829 parent, 690 child utterances

– English all utterances: 3160 adult, 5408 child

– age span is 1;2 to 2;6

A quantitative measure: coverage

• Goal: incrementally improving comprehension– At each stage in testing, use current grammar to analyze test set

• Coverage = % role bindings analyzed

• Example:

– Grammar: throw-ball, throw-block, you-throw– Test sentence: throw the ball.

• Bindings: scene=Throw, thrower=Nomi, throwee=ball• Parsed bindings: scene=Throw, throwee=ball

– Score test grammar on sentence: 2/3 = 66.7%

Learning to comprehend

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

Unified Cognitive Science

Neurobiology

Psychology

Computer Science

Linguistics

Philosophy

Social Sciences

Experience

Take all the Findings and Constraints Seriously

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 *Ellen Dodge (Ling) Michael Ellsworth (Ling) Joshua Marker (Ling) Shweta Narayan (Ling)

Alumni Terry Regier (UCB Ling,

CogSci) Johno Bryant (Ask) David Bailey (Google) Leon Barrett (Google) Nancy Chang (Sony Paris) Joe Makin (UCSF) Eva Mok (U. Chicago) Andreas Stolcke (ICSI, SRI) Dan Jurafsky (Stanford Ling) Olya Gurevich (Powerset) Benjamin Bergen (UCSD) Carter Wendelken (UCB) Srini Narayanan (ICSI, UCB) Steve Sinha (US Govt.) Gloria Yang (UCSF) Luca Gilardi (ICSI)

Source-Path-Goal

 

SCHEMA: spg

ROLES:

source: Place

path: Directed Curve

goal: Place

trajector: Entity

Translational Motion 

SCHEMA translational motion

SUBCASE OF motion

EVOKES spg AS s

ROLES

mover <-> s.trajector

source <-> s.source

goal <-> s.goal

CONSTRAINTS

before:: mover.location <-> source

after:: mover.location <-> goal

Event Structure for semantic QASrini Narayanan

• Reasoning about dynamics– Complex event structure

• Multiple stages, interruptions, resources, framing– Evolving events

• Conditional events, presuppositions.– Nested temporal and aspectual references

• Past, future event references– Metaphoric references

• Use of motion domain to describe complex events.• Reasoning with Uncertainty

– Combining Evidence from Multiple, unreliable sources– Non-monotonic inference

• Retracting previous assertions• Conditioning on partial evidence

Components of the System

• Object references– Fluents– Binder

• Short term storage– Predicate state

• Long term storage– Facts, mediators, what predicates exist

• Inference– Mediators

• Types– Ontology

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

The meaning pole may evoke schemas (e.g., image schemas) with a local alias. The meaning pole may include constraints on the schemas (e.g., identification constraints ).

construction TO

form selff.phon /thuw/

meaning evokes

Trajector-Landmark as tl Source-Path-Goal as spg constraints:

tl.trajectorspg.trajectortl.landmarkspg.goal

construction TO

form selff.phon /thuw/

meaning evokes

Trajector-Landmark as tl Source-Path-Goal as spg constraints:

tl.trajectorspg.trajectortl.landmarkspg.goal

Representing constructions: TO

local alias

identification constraint

TO vs. INTO:INTO adds a Container schema and appropriate bindings.

The INTO construction construction INTO

form selff.phon /Inthuw/

meaning evokes

Trajector-Landmark as tl Source-Path-Goal as spg

Container as cont constraints:

tl.trajectorspg.trajectortl.landmarkcontcont.interiorspg.goalcont.exteriorspg.source

An ECG analysis with THROW-TRANSITIVE