chapter 2: computer models of language

Chapter 2:Computer models of language

Cognitive Science José Luis Bermúdez / Cambridge University Press 2010

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


Putting them together: 3 case studies

• Terry Winograd and SHRDLU [TODAY]

• The imagery debate [MONDAY]

• Marr’s theory of vision [WEDNESDAY]


Background to SHRDLU

• Idea of language as:

• hierarchically organized • planned• rule-governed

• Computational approach to information-processing


Connections

•Direct analogy between formal languages and natural languages

+

•Hierarchical organization Recursion

•Plans Programs

•Rules Algorithms


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


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


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


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


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”)


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)


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


SHRDLU’s (virtual) microworld


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


Limitations1) 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


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)


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


(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)


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


The SENTENCE routine

• algorithmic

• calls upon other procedures

• not independent of semantics


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


“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


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


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

chapter 2: computer models of language

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