Chapter 2:
Computer models of language
The ingredients
•By now we have encountered some of the basic ideas feeding into
cognitive science
• move away from associationist models of
learning and behavior
• information theory as a tool for exploring the
nature and limits of cognitive abilities
• development of “boxological” accounts of how cognitive
tasks can be performed
• theory of computation as a model for
information-processing
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Putting them together: 3 case studies
• Terry Winograd and SHRDLU
[TODAY]
• The imagery debate
[MONDAY]
• Marr’s theory of vision
[WEDNESDAY]
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Background to SHRDLU
• Idea of language as:
•
•
•
hierarchically organized
planned
rule-governed
• Computational approach to information-processing
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Connections
•Direct analogy between formal languages and natural
languages
+
•Hierarchical organization
Recursion
•Plans
Programs
•Rules
Algorithms
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Recursion
•Basic idea in logic and computer science
•
recursive definition
•
recursive procedures
•Recursion is a tool for understanding the relation between
complex structures and the simple elements from which
they are composed
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Recursive definition of wffs
•Well-formed formula in propositional logic: contains infinitely many
propositional symbols P0, P1, . . ., together with connectives ‘’, ‘’,
‘’, and ‘’
•
•
•
•
For all i, ‘Pi’ is a wff
If ‘Q’ is a wff, then ‘ Q’ is a wff
If ‘Q’ and ‘R’ are wffs, then ‘Q  R’ is a wff
Etc Etc
•Generates an algorithmic procedure for checking whether a given
formula is a wff
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Recursion in natural language
– The mouse the cat bit ran away
– The mouse the cat the dog chased bit ran away
– The mouse the cat the dog the man frightened chased bit ran
away
•Exploit the recursive rule allowing center-embedding
•Problematic for associationist models – because
agreement between nouns and verbs depends upon what
comes later in the sentence
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Natural language processing
•Branch of AI tackling the challenge of building a machine
that can speak and understand natural languages
•
Engineering dimension – computers that can use
language to interact with people
•
CogSci dimension – understand the
computational basis of human linguistic abilities
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Early examples
•Chatterbots (e.g. ELIZA)
• typically use canned reponses to simulate
conversation
•Machine translation
• wave of enthusiasm in the 1950’s
• fundamental problems with ambiguity
• tried to deal with them using statistical semantics
("a word is characterized by the company it keeps”)
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Winograd’s SHRDLU
•Narrowed down the computational challenge by restricting
communication to a micro-world
•Interaction with the micro-world introduced a semantic
dimension – allowed the application of language to the world
•Built on a procedural model of information-processing
•Constructed according to basic boxological principles (with a
twist)
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Micro-worlds
• Microworlds are artificial domains where all the possible objects,
properties and events are defined in advance
•
Many (all?) of the successes in AI have taken place in
microworlds (chess, data-mining, logistics planning, credit card fraud
detection. . .)
•
•
Microworlds narrow down the task domain –
But they also make the program more general
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
SHRDLU’s (virtual) microworld
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
What SHRDLU can do
1) Answer questions about the block-world
2) Resolve ambiguities
3) Carry out instructions via a (simulated) robot hand
4) Develop plans for carrying out complex instructions
5) Incorporate new words into its vocabulary
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Limitations
1) Purely reactive – has no goals except those introduced in
the “conversation”
2) No sensitivity to the pragmatics of the conversation
3) Only capable of questions and declarative sentences
4) No need for context-sensitivity
5) Highly simplified grammar and vocabulary
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Haugeland on SHRDLU
•SHRDLU performs so glibly only because his domain has been
stripped of anything that could ever require genuine wit or
understanding. Neglecting the tangled intricacies of everyday life
while pursuing a theory of common sense is not like ignoring
friction while pursuing the laws of motion; its like throwing the baby
out with the bathwater. A round frictionless wheel is a good
approximation of a real wheel because the deviations are
comparatively small and theoretically localized: the blocks-world
“approximates” a playroom more as a paper plane approximates a
duck.
•(Artificial Intelligence: The Very Idea p. 190)
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Is Haugeland being unfair?
•SHRDLU isn’t part of a theory of common sense - nor is it
intended as a complete model of natural language
understanding
•SHRDLU shows how far we can get in modeling language
understanding using certain basic tools
•
•
(quasi-)hierarchical architecture
procedural/algorithmic semantics
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
(Quasi-)hierarchical architecture
•SHRDLU is heterarchically organized
•
composed of 12 separate sub-systems
•
each sub-system has a specific task to perform
•But there is no overall “controller” & the sub-systems do not
work in sequence  cross-talk allows sub-systems to recruit
outputs of other sub-systems
•Sub-systems are made up of sets of procedures (algorithmic
routines for solving specific problems)
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Overall architecture
•Group 1 sub-systems Syntactic analysis
•Group 2 sub-systems Semantic analysis
•Group 3 sub-systems Integrating and using
information/planning
responses to commands
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
The SENTENCE routine
• algorithmic
• calls upon other
procedures
• not independent of
semantics
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Syntax and semantics
•Separation of syntax and semantics is a basic principle of much
theoretical linguistics and cognitive science
•
Chomskyan generative grammar is a purely
syntactic theory
•
Cognitive scientists often assume that there is a dedicated
“syntactic parsing system”
•But Winograd’s heterarchical approach rejects the separation
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
“Put the blue pyramid on the block in
the box”
•Ambiguous: Is the blue pyramid already on the block?
Is the block already in the box?
•This ambiguity cannot be resolved purely syntactically
•
In fact, the syntactic sentence parser cannot run until
the ambiguity is resolved
•
But the semantic analysis also requires syntactic input
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Stages in parsing sentence
1) The parser produces “the blue pyramid on the block” as
candidate NP  calls semantic analysis procedure
2) SHRDLU checks whether there is a block with a blue
pyramid on it.
3) There isn’t. So SHRDLU backtracks to the parser,
which suggests “the blue pyramid”
4) This works. So SHRDLU takes “the block in the box” as
the object of the imperative “put”
5) Calls semantic analysis procedure to check that there
is a block in the box
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010
Take-home messages
• SHRDLU avoids many of the problems of “real-life”
language comprehension (due to the way the block-world
is built up)
• But it shows what can be done with quasi-hierarchically
organized and algorithmic routines
• Illustrates how we can understand a complex cognitive
ability as the product of a set of computational processes
Cognitive Science
 José Luis Bermúdez / Cambridge University Press 2010