csctr session 8 dana retová. group at uc berkeley & uni of hawaii nancy chang benjamin bergen...
TRANSCRIPT
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Embodied construction grammar
CSCTR Session 8Dana Retová
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group at UC Berkeley & Uni of Hawaii
◦ Nancy Chang◦ Benjamin Bergen◦ Jerome Feldman, …
General assumption◦ Semantic relations could be extracted from language input
“In its communicative function, language is a set of tools with which we attempt to guide another mind to create within itself a mental representation that approximates one we have.” (Delancey 1997)
NTL
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Listener and speaker have to share enough experience
Language can be expressed by a discrete set of parameters and by semantic relations among entities and actions.◦ How these relations are encoded in the sequences
of letters and sounds?
Language
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1. A word that conveys some meaning◦ “in, on, through”
2. Word order◦ “red fire engine” vs. “fire engine red”
3. Some change in a base word ◦ -”ed” ending for the past tense◦ Systematic change in spelling (“car”-> “cars”)◦ Converting a verb to a noun (“evoke”-
>”evocation”)
3 mechanisms for conveying a semantic relation
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Context Free Grammar
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Analysis of simple sentence by CFG
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S -> VP NP◦ VP.person <-> NP.person◦ VP.gender <-> NP.gender◦ VP.number <-> NP.number
Solution
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Context◦ The meaning of indexicals
“here”, “now”◦ Referents of expressions
“they”, “that question”◦ Ambiguous sentences
“Harry waked into the café with the singer”◦ Metaphors◦ Intonation (e.g. stress, irony,…)
“HARRY walked into the café.” “Harry WALKED into the café.”
◦ Gestures
What CFG cannot cover?
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Language understanding
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Meanings reside in words◦ Each word has multiple fixed meanings – word
senses◦ Rules of grammar are devoid of meaning and only
specify which combinations of words are allowed Meaning of any combination of words can be
determined by first detecting which sense of each word is involved and then using the appropriate rule for each word sense.◦ “stone lion”◦ Should each animal name like “lion” have another
word sense covering animal-shaped objects
Traditional theory
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Each word activates alternative meaning subnetwork
These subnetworks themselves are linked to other circuits representing the semantics of words and frames that are active in the current context.
The meaning of a word in context is captured by the joint activity of all of the relevant circuitry
NTL – alternative theory
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To write down rules of grammar that are understandable by people and computer programs and that also characterize the way our brains actually process language
The job of grammar is to specify which semantic schemas are being evoked, how they are parameterized and how they are liked together in the semantic specification.
To formalize cognitive linguistics
Goal of NTL’s embodied grammar
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Construction = pairing of linguistic form and meaning◦ All levels of linguistic form (prefixes, words, phrases,
sentences, stories, etc.) can be represented as mapping from some regularities of form to some semantic relations in the semantic specification
“embodied”◦ Semantic part of a construction is composed of
various kinds of embodied schemas Image Force dynamic action
Embodied construction grammar
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Simulation-based language understanding
Analysis Process
SemanticSpecification
“Harry walked into the cafe.” Utterance
CAFE Simulation
Belief State
General Knowledge
Constructions
construction WALKEDform
selff.phon [wakt]meaning : Walk-Action constraints
selfm.time before Context.speech-time selfm..aspect encapsulated
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“Harry strolled to Berkeley” Individual word
◦ simplest construction (lexical)
Lexical construction To |Fromsubcase of Spatial Prepositionevokes SPG as sform “to” |“from”meaning Trajector-Landmark
lm <-> s.goal |lm <-> s.sourcetraj <-> s.traj
Example
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Construction Spatial PPsubcase of Destinationconstituents
r: Spatial Prepositionbase: NP
form r < basemeaning
r.lm <-> base
In CFG: Spatial PP -> Spatial Preposition NP
Spatial Prepositional Phrases
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SemSpec for “Harry strolled into Berkley”
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Lexical construction Harrysubcase of NPform “Harry”meaning Referent Schema
type <-> persongender <-> malecount <-> onespecificity <-> knownresolved <-> harry2
“Harry”
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SemSpec for “Harry strolled into Berkeley”
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Lexical construction Strolledsubcase of Motion Verb, Regular Pastform “stroll+ed”meaning WalkX
speed <-> slowtense <-> pastaspect <-> completed
“Strolled”
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Only single parameter controls the rate of moving one leg after the other
Leg moves only after the other is stable◦ As opposed to running
WalkX schema
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SemSpec for “Harry strolled into Berkeley”
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Lexical construction Strolledsubcase of Motion Verb, Regular Pastform “stroll+ed”meaning WalkX
speed <-> slowtense <-> pastaspect <-> completed
“Strolled”
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SemSpec for “Harry strolled into Berkley”
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Construction Self-Directed Motionsubcase of Motion Clauseconstituents
movA: NPactV: Motion VerblocPP: Spatial PP
form mover < action < directionmeaning Self-Motion Schema
mover <-> movAaction <-> actVdirection <-> locPP
Self-directed motion
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SemSpec for “Harry strolled into Berkley”
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ECG’s formalized schemas are just a way of writing down hypothesized neural connections and bindings.
These schemas are connected to semantic specification (SemSpec).
The simulation semantics process uses SemSpec and other activated knowledge to achieve conceptual integration and the resulting inferences
What is the difference between ECG and other formal notations of gramar rules?
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Normally “sneeze” is intransitive Traditional grammar would suggest separate word sense for
sneeze as a transitive verb ECG would need caused motion construction
Construction Caused Motionsubcase of Motion Clauseconstituents
causer: Agentaction: Motiontrajector: Movable objectdirection: SpatialSpec
form causer < action < trajector < directionmeaning Caused Motion Schema
causer <-> action.actordirection <-> action.location
“She sneezed the tissue off the table”
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In traditional view “opened” refers to one sense of beer while “drank” to another
“Beer” sometimes stands for a “container of beer” In ECG we use measure phrase construction
Construction Measure NPsubcase of NPconstituentsm: Measure NP“of”s: Substance NPform m < “of” < smeaning Containment Schemavessel <-> mcontents <-> s
“She opened and drank an expensive large beer”
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1. Schema2. Construction3. Map
metaphors4. Mental space
Can formalize “Josh said that Harry strolled to Berkeley”
Talking about other times, places, other people’s thoughts, etc.
4 basic formal structures to formalize cognitive linguistics
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Computer understanding systems◦ Narayanan (1997)
Analysis of metaphors in news articles Used pre-processed semantics
◦ Bryant (2004) Program that derives semantic relations that underlie
English sentences Later Bryant, Narayanan and Sinha combined the
two models
Use of ECG
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Human processing:◦ What can ECG tell us about natural intelligence?
◦ Garden-path sentences “The horse raced past the barn fell” Narayanan et al. 1988 – computer model that gives detailed
predictions of how various factors (frequency of individual words, likelihood that they appear in certain constructions, etc.) interact in determining the difficulty of a garden-path situation.
“The witness examined by the lawyer turned out to be unreliable” “The evidence examined by the lawyer turned out to be unreliable”
◦ Chang (2006) Model how children learn grammar
Use of ECG
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Learning constructions
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First words
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Image schemas◦ Topological
E.g. a container◦ Orientational
E.g. “in front of”◦ Force-dynamic
E.g. “against”
Reference object and smaller object◦ Landmark and trajector
Understanding prepositions
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English
ON
AROUND
OVER IN
Bowerman & Pederson
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Dutch
Bowerman & Pederson
ANN
OM
BOVEN IN
OP
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Chinese
Bowerman & Pederson
SHANG
ZHOU
LI
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“Into” binds inside to a goal
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Language and thought ◦ “El jamón prueba salado“
Computational models Connectionist networks Neural systems
Levels of description
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Emulates a child viewing a simple geometric scene and being told a word that describes something about that scene
Has universal structure – visual system◦ 2 classes of visual features
Quantitative geometric features (e.g. angles) Qualitative topological features (e.g. contact)
◦ Components Center-surround cells, edge-sensitive cells, etc.
Trained with a series of word-image pairs Standard back-propagation learning Later extended with motion prepositions (into, through,
around)
Reiger (1996)
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Model
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Children perform and plan actions long before they learn to describe them
Idea of characterizing actions by parameters◦ Motor control has its hierarchy
Lower level Coordination, inhibition
Higher level Desired speed
◦ We can create abstract neuralmodels of motor controlsystems executing schemas
Action words
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“Push” and “walk” schemas
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Child learning of action words◦ Performing an action and hearing her parent’s
label Restricted to actions that can be carried out
by one hand on a table
Bailey (1997)
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Intermediate set of feature structures◦ Parameterization
of action◦ Chosen to fit the
basic X-schemas Bi-directional
arrows◦ Labeling pathway◦ Command
pathway
Model
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4 steps in learning “push”
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Model how children learn their first rules of grammar and generalize them in more adult-like rules
Chang (2006)
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Suppose the child knows lexical construction for words “throw” and “ball”
But does not know construction for the phrase “throw ball”
“You throw the ball”
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She learned that the second word determines which object fills the thrown role of a throw action
Only later learns generalization of this construction that works for any transitive verb
New grammar rule
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Key to understanding grammar acquisition is not the famous poverty of stimulus but rather the richness of the substrate◦ Child already has rich base of conceptual and
embodied experience The reason why understanding is ahead of
production◦ Child can understand complex sentences by
matching constructions to only parts of the utterance
◦ Constructions are the same in both
Grammar learning
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Decay of unused knowledge◦ People always choose the set of constructions
that best fits an input◦ If you keep track of best matches and
Increase the potential value of successful constructions
Decrease probability of trying not-useful constructions
◦ There would always be a better choice Best-match
◦ Given a sentence S and a grammar G, the best analysis is the one that maximizes the probability of sentence S being generated by grammar G
No need for negative evidence
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Lifting (learning superordinate categories)◦ Taking a collection of relations of similar form and
replacing the common element with a parameter After learning that cows, dogs, horses and pigs all
move and eat and make noises, a good learning system will postulate a category (animal) and just remember what goes in the category and what relations to apply to members
Occurs also in grammar learning◦ Very early child generalizes e.g. throw-ball to
other small objects
Generalisation