A Mechanism for Processing Argumentative Discourse - CiteSeerX

CS{1990{23

A Mechanism for Processing Argumentative Discourse Curry I. Guinn

Department of Computer Science Duke University Durham, North Carolina 27708{0129 December 6, 1990

A Mechanism for Processing Argumentative Discourse Curry I. Guinn

December 6, 1990

Supervised by Alan W. Biermann Thesis submitted in partial ful llment of the requirements for the degree of Master of Science in the Department of Computer Science in the Graduate School of Duke University

This document is a reformatted version of the thesis, and equivalent in content.

Copyright c 1990 by Curry I. Guinn All rights reserved

Abstract The debater system is an implementation of a model for argumentative discourse which aids in processing argumentative discussions on both the discourse level (the processing of what is said) and the surface syntactic level (the processing of certain syntactic phenomena like anaphora). The mechanism developed for processing argumentative discourse is called a multi-stack hierarchy. This tree-like structure proves useful in evaluating statements in the context of a debate and in responding to arguments in a coherent, organized manner. The multi-stack hierarchy provides a mechanism superior to Augmented Transition Network (ATN) grammars for processing discussions. The multi-stack hierarchy also can account for the usage of pronominal references as well as the presence of deictic references. These accounts are more satisfactory than explanations of pronominal and deictic references using a single focus stack. The multi-stack hierarchy is an amalgam of the structures postulated by Cohen and Birnbaum et al. to process argumentative discourses with the structure Reichman uses to account for certain surface syntactic level phenomenon.Overall, this approach to discourse processing is consistent with Grosz and Sidner's division of discourse into three separate but interrelated parts: the intentional structure, the linguistic structure, and the attentional state.However, instead of maintaining one structure to represent the intentional structure and another to maintain the attentional state, the multi-stack hierarchy provides a single, relatively simple structure, to handle both tasks.

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Acknowledgements I would like to thank my advisor, Dr. Alan Biermann, for his support and advice throughout this project. I am also grateful to those who enriched my knowledge of natural language processing through discussion: Heidi Brubaker, Dania Egedi, D. Richard Hipp, Barry Koster, and Ronnie Smith. Special thanks go to my wife, Virginia Holman, for her loving support throughout these years.

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Contents Abstract Acknowledgements 1 Introduction

1.1 Discourse Level Phenomena 1.1.1 Parallel Conversation 1.1.2 Multiple Arguments 1.1.3 Hidden Arguments 1.1.4 Cohesiveness 1.2 Surface Level Phenomena 1.2.1 Pronoun and Deictic Reference Use

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2 Related Work

2.1 Grosz and Sidner's Model 2.1.1 The Single Stack Approach to Processing the Attentional State 2.1.2 Problems with the Single Stack Approach 2.2 Reichman's ATN Model 2.2.1 Context Spaces 2.2.2 The ATN Model of Intentional Structure 2.3 Models of Argumentation

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3 The Multi-stack Hierarchy

3.1 The Multi-Stack Data Structure 3.2 The Multi-Stack Approach and the Maintenance of Focus 3.2.1 Discourse Segment Status Assignments 3.2.2 Focus Assignments 3.2.3 Pronominal and Deictic Reference Rules 3.3 The Multi-Stack Approach and Discourse Control 3.3.1 The \Game" of Arguing 3.3.2 A Trace of a Debate

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4 The debater System

4.1 Parsing 4.2 The Database 4.2.1 The Problem of Determining Relationships Between Segments 4.2.2 The Argument Database 4.3 Discourse Rules

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CONTENTS

vi 4.3.1 Verbosity of Response 4.3.2 Some Debate Heuristics 4.4 debater in Action

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5 Future Research 6 Conclusions A Sample Debates

A.1 Debate Example 1 A.2 Debate Example 2 A.3 Debate Example 3

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List of Figures 1.1 A Sample Debate 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9

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Grosz and Sidner's Three Components of Discourse Structure Grosz and Sidner's Discourse Segments, Focus Stack Space and Dominance Hierarchy Usage of the Focus Stack Parallel Conversation A Dialogue with a Deictic Reference Reichman's Context Space Hierarchy A Portion of Reichman's ATN Model of Discourse A Typical Tree Representation for Cohen A Typical Argument Structure for Birnbaum : : : : : : : : : : : : :

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3.1 A Sample Discourse 3.2 A Comparison Between the Single Stack and the Multi-stack 3.3 A Sample Debate

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4.1 General Representation of the debater System

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LIST OF FIGURES

List of Tables 2.1 Status Assignments

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LIST OF TABLES

Chapter 1

Introduction In order for computers to process and engage in natural language discourse, it is necessary to develop models of discourse structure. These discourse models should aid in explaining actual discourse phenomena. A great deal of work in linguistics has studied phenomena at the sentence level. Rules have been stipulated which help explain word order, pronoun use, and many other syntactic and semantic phenomena. However, computational models of natural language have not been very successful at processing multiple sentence utterances. It appears that a great deal of the processing of a paragraph, a sentence, a phrase, or even a word requires an understanding of the context of that element. Quickly, the need for models concerning the context of linguistic elements becomes obvious. The debater system is an implementation of a model for argumentative discourse which aids in processing argumentative discussions on both the discourse level (the processing of what is said) and the surface syntactic level (the processing of certain syntactic phenomena like anaphor). In the debate in Figure 1.1, there are many examples of discourse level phenomena and surface level phenomena which must be accounted for. Discourse level phenomena include parallel conversation, multiple arguments, hidden arguments, and cohesiveness. Surface level phenomena include pronoun and deictic reference use.

1.1 Discourse Level Phenomena There are four types of discourse level phenomena dealt with in this model: parallel conversation, multiple arguments, hidden arguments, and cohesiveness.

1.1.1 Parallel Conversation

In sentences 2-6, it is almost as if John and Mary are in two dierent conversations. Although connected to the original statement that handguns should be banned, each debater argues for dierent points, seemingly ignoring the other's arguments. This sort of parallel conversation occurs regularly in debates, and the discourse model must be able to process this interaction.

1.1.2 Multiple Arguments

John puts forth two arguments in sentences 7 and 8. Conversation is clearly not limited to one sentence interactions. The discourse model must be able to process multiple argument utterances. 1

CHAPTER 1. INTRODUCTION

2 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

John: Handguns should be banned. Mary: But with a gun a woman can defend herself against a rapist. John: There will be fewer murders if handguns are banned. Mary: You haven't addressed my argument. John: They have gone down in other countries after a strict handgun

ban has been put in place. Mary: Look, if one comes in my home, I want to have a pistol handy. John: A gun could be turned against a homeowner. John: That criminal could turn your gun against you. Mary: Well, it's stupid to ban handguns. Mary: Criminals won't worry about the misdemeanor of having a banned item. John: They won't be able to get guns if there are none available to steal. Figure 1.1: A Sample Debate

1.1.3 Hidden Arguments

In sentence 11, John does not directly attack Mary's statement that criminals will not worry about a misdemeanor. Rather, John attacks an apparent presupposition of Mary's: criminals will still be able to get guns. Certainly, a model of argumentative discourse must be able to handle unsaid presuppositions or hidden arguments.

1.1.4 Cohesiveness

Cohesiveness is a property of the general structure of a debate or conversation. If a computer is to interact with natural languages, the model it uses must be sucient to insure that the conversation makes sense, that utterances are understandable in the context of other utterances in the debate.

1.2 Surface Level Phenomena Any model of discourse must be able to account for the usage of pronouns and deictic references. What conditions must be met in order to use a particular pronoun or reference?

1.2.1 Pronoun and Deictic Reference Use

There are several instances of pronoun use: sentences 5, 6, and 10. How these pronouns are resolved must be explained. The pronoun, they, in sentence 5 is indexed to the noun phrase murders in sentence 3, while one in sentence 6 is indexed back to the noun phrase a rapist in sentence 2. How these indexings are possible must be explained.

1.2. SURFACE LEVEL PHENOMENA

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The use of the deictic reference, that criminal, in sentence 8 must also be accounted for. Why does the use of the pronoun, he, in its place seem ungrammatical.

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CHAPTER 1. INTRODUCTION

Chapter 2

Related Work 2.1 Grosz and Sidner's Model Grosz and Sidner explored a theory of discourse structure that is composed of three separate but interrelated parts: the linguistic structure, the intentional structure, and the attentional state. Figure 2.1 expresses this relationship. Linguistic Structure

Intentional Structure

Attentional State

Figure 2.1: Grosz and Sidner's Three Components of Discourse Structure The linguistic structure is the structure of the sequence of utterances that comprise a discourse. Discourses are divided into aggregates called discourse segments. Utterances in a

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CHAPTER 2. RELATED WORK

segment serve particular roles within that segment, and segments have relationships to the other segments in the discourse. That discourses are divided into segments has been observed in a variety of discourse types [1][7][10][13][17][18]. The intentional structure represents the purposes that underlie the discourse. Each segment has a discourse segment purpose (DSP). The DSP speci es how the segment relates to the overall discourse purpose. Grosz and Sidner discuss the dominance hierarchy which orders the DSPs. Some DSPs contribute to the satisfaction of other DSPs. Suppose DSP1 contributes to the satisfaction of DSP2. Then we say DSP2 dominates DSP1, and DSP1 satisfaction-precedes DSP2. The attentional state is an abstraction of the participants' focus of attention as the discourse unfolds. The attentional state is modeled by a set of focus spaces. These spaces contain information which is necessary to process new elements of the discourse. Focus spaces will contain entities that are recently under discussion and information about the current DSP. Figure 2.2 contains the example gures given by Grosz and Sidner to illustrate the structures. Part (a) shows the focus space stack after DS1 and DS2 have been processed. Note that DSP1 dominates DSP2. Part (b) shows the focus space stack after DS3 is processed. Note that DSP2 has been popped o the stack as DSP2 does not dominate DSP3. (However, DSP1 does dominate DSP3.)

2.1. GROSZ AND SIDNER'S MODEL Discourse Segments DS1 ..... ........ DS2.... ..... DS3 ... .....

Focus Space Stack Properties Objects Relations DSP2 Fs2 Properties Objects Relations DS1 Fs1

7 Dominance Hierarchy

DSP1 Dominates DSP2

(a) Discourse Segments DSP1 ..... ........ DS2.... ..... DS3 ... .....

Focus Space Stack Properties Objects Relations DSP3 Fs3 Properties Objects Relations DSP1 Fs1

Dominance Hierarchy

DSP1 Dominates DSP3 DSP1 Dominates DSP2

(b) Figure 2.2: Grosz and Sidner's Discourse Segments, Focus Stack Space and Dominance Hierarchy

2.1.1 The Single Stack Approach to Processing the Attentional State

Using a stack to process the attentional state has been shown to be quite eective in processing some anaphora [1][11]. The following example for James Allen exempli es the use of a stack in resolving pronouns.

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E: Now attach the pull rope to the top of the engine. E: By the way, did you buy gasoline today? A: Yes. I got some when I bought the new lawnmower wheel. A: I forgot to take the gas can with me, so I bought a new one. E: Did it cost much?

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6 A: No, and we could use another anyway to keep with the tractor. 7 E: OK, how far have you got? 8 E: Did you get it attached? As Allen points out, the pronoun it in sentence 8 refers to something mentioned seven sentences earlier. There are several objects mentioned that thematically could be attached. How is the right referent obtained? The focus stack approach aids in facilitating the proper matching. Sentence 1 creates a new segment to be pushed on the focus stack. Sentences 2-6 constitute a subdialogue which creates segments which are pushed on top of the focus stack. Sentence 7 triggers a pop which removes everything o the stack except for the rst segment. Thus the reference to it is easy to nd. Figure 2.3 nds demonstrates the operations.

2.1. GROSZ AND SIDNER'S MODEL

Focus Stack After Processing Sentence 1 Focus Space with information about segment 1 FS1 Focus Stack After Processing Sentence 6 Focus Space with information about the segments in the subdialogue 2-6 FS2-6 Focus Space with information about segment 1 FS1 Focus Stack After Processing Sentence 7 Focus Space with information about segment 1 FS1 Focus Stack After Processing Sentence 8 Focus Space with information about the segment 8 FS8 Focus Space with information about segment 1 FS1 Figure 2.3: Usage of the Focus Stack

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2.1.2 Problems with the Single Stack Approach

There are several examples that seem to indicate that the single focus stack is inadequate. Grosz and Sidner mention the ashback problem. There seem to be other problems as well.

Multiple Arguments Often in discourse, a speaker introduces multiple arguments. If a single stack is used to process such discourses, it seems that some information may be lost because of a popped focus segment that should have remained. Here is a simple example: 1. 2. 3. 4. 5.

Mary: I think that handguns should not be banned. Mary: The constitution guarantees every citizen the right to Mary: and, anyway, I don't think that the streets will be any John: Look, the constitution does not guarantee that right; John: it only guarantees that states have the right to form National Guard.

bear arms, safer. militias like the

After processing Sentence 2, the segment stack would be as before: Segment 2 Segment 1 The question now is what to do with Segment 3. Segment 3 seems to be a support of Segment 1. It does not have any obvious relation to Segment 2. With a single stack, Segment 2 must be popped o and Segment 3 pushed on. However, this leads to an obvious problem when Sentence 4 is processed. The deictic reference that right could not be resolved appropriately now that Segment 2 is no longer on the stack.

Parallel Conversations Another problem for the single focus stack occurs when there is a \parallel conversation" as sentences 2-6 in Figure 1.1 reprinted partially here in Figure 2.4: Note that John makes a pronominal reference in Sentence 5 to an object in Sentence 3 while Mary makes a pronominal reference in sentence 6 to an object in Sentence 2. Clearly, a single stack could not handle this sort of phenomenon. Deictic References A third problem for the single focus stack is in the resolution of deictic

references (like this book, those men, that car). The example presented in gure 2.5 illustrates the diculty with how to resolve these references with a single stack. The fact that he (if indexed to one in Sentence 3) does not seem grammatical in the place of that criminal in Sentence 5 is troublesome. If the focus space created by Sentence 5 is dominated by the focus space created by Sentence 3, the use of he should be quite valid. However, if the focus space created by Sentence 5 is not dominated by the focus space created by Sentence 3, by what means is the reference that criminal satis ed? Clearly, this dichotomy between the usage of pronominal and deictic references must be accounted for.

2.1. GROSZ AND SIDNER'S MODEL

1. 2. 3. 4. 5.

John: Handguns should be banned. Mary: But with a gun a woman can defend herself against a rapist. John: There will be less murders if handguns are banned. Mary: You haven't addressed my argument. John: They have gone down in other countries after a strict handgun ban

in place. 6. Mary: Look, if one comes in my home, I want to have a pistol handy. 7. John: A gun could be turned against a homeowner. Figure 2.4: Parallel Conversation

1. 2. 3. 4. 5.

John: Handguns should be banned. Mary: But with a gun a woman can defend herself against a rapist. Mary: If one comes in my home, I want to have a pistol handy. John: A gun could be turned against a homeowner. John: That criminal could turn your gun against you. Figure 2.5: A Dialogue with a Deictic Reference

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2.2 Reichman's ATN Model

Rachel Reichman oers a model of discourse with two distinct parts: an augmented transition network (ATN) to model higher level discourse processes and a graph of context spaces to aid in processing surface linguistic phenomenon[17]. The ATN grammar corresponds to Grosz and Sidner's intentional structure while the graph of context spaces corresponds to the attentional state.

2.2.1 Context Spaces

Reichman's context spaces roughly correspond to discourse segments. Each discourse segment creates a context space which contains information that de nes the context space. Each context space has the following slots: Type: The context space's category name (Examples include support, issue, nonnarrative support, etc.). Derivation: Whether the context space was explicitly stated or implicitly derived. Goal: The function served by the space (Examples include support, clari cation, etc.). Focus: Speci es the in uential status of elements within that context Context spaces contain other information which details relations to other context spaces. Context spaces are arranged in a hierarchy which re ects their relationships with each other. The dialogue below concerning the behavior of children has been divided by Reichman into the context space hierarchy given in Figure 2.6. 1. R: Except, however, John and I just saw this two-hour 2. R: TV show 3. M: Uh hum 4. R: where they showed { it was an excellent French 5. R: TV documentary { and they show that in fact 6. R: the aggressive nature of the child is not really 7. R: that much in uenced by his environment. 8. M: How did they show that? 9. R: They showed that by lming kids in kindergarten, 10. M: Uh hum, 11. R: showing his behavior among other children, 12. M: And then? 13. R: and showed him ten years later acting the same way, 14. R: toward, um, 15. D: Well, of course, that's where he learns his 16. D: behavior, in kindergarten.

2.2. REICHMAN'S ATN MODEL

CS1: Lines 6-7 Aggressive nature of child not in uenced by environment. CS2: Lines 1-5,9,11, 13-14. Documentary study, 10 years after kindergarten same behavior CS3: Lines 15-16 Behavior learned in kindergarten

CS2 and CS3 are subconstituents of CS1 (Support and Challenge) Figure 2.6: Reichman's Context Space Hierarchy

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Each context space has a status assignment which re ects the in uential status of the context space. These status assignments are dynamic; a context space may at one time be generating and at another time be closed. Table 2.1 lists the status assignments recognized by Reichman and their meaning. Active

The context space in which current utterances are being placed. There can be only one active context space at a time. Controlling The context space in direct relation to the active context space being developed. Precontrol A previously controlling context space whose controlling status has been usurped. Open A previously active context space that was interrupted before completion of its conversational move. Generating A context space in indirect relation to which an active context space is being developed. A context space containing an initiating claim of some subargument has a generating status value while the subargument is in progress. Closed A context space whose discussion is believed completed for the present time. Table 2.1: Status Assignments Elements in the discourse are assigned a level of focus (high, medium, low, zero) depending on the grammatical use of that element and the status of its context space. Some examples of focus assignments include: 1. The entity referred to by the grammatical subject of an utterance, the subject of a thereinsertion, the subject of a pseudocleft, cleft, or topicalized clause is assigned a high focus level assignment. 2. A nonsubject referenced by name is assigned a medium focus level assignment. 3. A nonsubject referenced by description is assigned a low focus. 4. The focus level assignments of entities in a digression space which were mentioned in the interrupted space keep the same assignment. 5. An entity referenced by name after previous references by description is reassigned a high focus. The level of focus of an element combined with the status of its context space determines how that element may be referenced. Reichman presents the following four rules which determine element referencing: R1. Only elements in high focus in a currently active or controlling context space may be referenced pronominally. R2. Only elements in a currently active or controlling context space may be referenced by a close deictic referring expression. R3. Full de nite descriptions are needed to reference elements in a closed context space.

2.2. REICHMAN'S ATN MODEL R4. Only far deictic or full descriptive referring expressions can be elements of a generating context space.

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Problems with Reichman's Context Space Approach These rules seem too rigid to accommodate all the data. The following short dialogue seems to insubstantiate the rst rule, R1: John: My brother threw a rock at a dog. Pete: Did it attack? Parallel conversations also present problems for this approach. Clearly the dialogue presented in Figure 2.4 violates R1. However, R4 seems to be a good account of the use of the deictic in the dialogue presented in Figure 2.5.

2.2.2 The ATN Model of Intentional Structure

Reichman proposes an Augmented Transition Network (ATN) grammar to represent the intentional structure of a conversation. An ATN consists of network of states and arcs which link states. In sentence grammars the basis unit is the word. In a discourse grammar, for Reichman, the basic unit is the conversational move. Figure 2.7 represents a portion of Reichman's ATN representation of discourse.

Problems with the ATN Model

The discourse grammar approach to modeling intentional structure has received a great deal of criticism [1][9][11]. Grosz and Sidner state that The intentions that underlie discourse are so diverse that approaches to discourse coherence based on selecting discourse relationships from a xed set of alternative rhetorical patterns are unlikely to suce. Instead they introduce a small number of structural relations which hold between intentions (like support and generate). The ATN model does not easily account for multiple argument attacks and supports, parallel conversation, and hidden arguments.


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Logical Abstraction

Interruption Choice

CommentChoice

Nondevelopmental Choice

Support Choice/Step 1

DevelopmentalChoice/Step 2 Produce Next-Move

Transitional Utterance

DevelopmentalChoice/Step 1 SupportChoice/Step 2

StartDebate

FurtherChallenge/ Steps 1-5 ChallengeChoice/Step 1 Further ChallengeSupport/Step 3

ChallengeChoice/Step 2

ChallengeChoice/Step 3

Figure 2.7: A Portion of Reichman's ATN Model of Discourse

2.3. MODELS OF ARGUMENTATION

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2.3 Models of Argumentation Robin Cohen gives an analysis of argumentative discourse which organizes the intentions of the discourse into a tree hierarchy[7]. For example, the following discourse: 1. The city is a mess. 2. The parks are a mess. 3. The park benches are broken. 4. The grassy areas are parched. 5. Returning to city problems, the highways are bad too. has the tree representation given in Figure 2.8. 1 2 3

5 4

Figure 2.8: A Typical Tree Representation for Cohen The relationships between the segments are simple; in this case, 2 and 5 are supports of 1 while 3 and 4 are supports of 2. This organization of discourse has several advantages: It is useful for measuring the eciency of the model since operations are on a tree structure. The characterization shows the coherency of the dialogue. There is greater exibility in the handling of multiple arguments, parallel conversations, and hidden arguments than with an ATN model. Birnbaum, Flowers, and McGuire also explore a similar model of argumentation, showing how a tree representation is useful in processing and responding to argumentative statements[3][2]. Birnbaum et al. give the following segment of a debate: 1) Arab: Who started the 1967 War? 2) Israeli: (a) The Arabs did, (b) by blockading the Straits of Tiran, (c) and blockades are an act of war. 3) Arab: But Israel attacked rst.


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The tree structure for 1, 2(a), 2(b) and 2(c) is given in Figure 2.9. Search rules operate on the argument graph in order to respond to an opponent's utterances. These rules are tactics in determining how to respond. For example, one search rule embodies three ways to attack a support: (a) Attack the main point directly. (b) Attack the supporting statement. (c) Attack the claim that the evidence gives support for the main point. In the above debate, the Arab speaker traverses up the support links in order to attack the Israeli's position. In Birnbaum et al.'s database the Arab could not attack 2(c) as it considered blockades being an act of war to be a fact. The search continues up the tree to 2(b), but the speaker nds it cannot attack this node either. The speaker then arrives at 2(a) which the speaker attacks (with 3). This model of argumentation provides good support for discourse coherence. Following the support and attack paths seems to be a logical and accurate description of how arguments proceed. 2a) The Arabs started the war. support 2b) The Arab blockade led to the war.

attack 3a) The Israelis started the war.

support 2c) Blockades are acts of war. Figure 2.9: A Typical Argument Structure for Birnbaum

Chapter 3

The Multi-stack Hierarchy The structure Reichman uses to process syntactic phenomena closely resembles the structures Cohen and Birnbaum et al. use to process higher level discourse phenomena. In this chapter, a mechanism will be presented which uses the same structure to process both surface linguistic phenomena as well as higher level discourse phenomena. Using this single structure to process the two levels is bene cial for several reasons: The relationship between sentential-level phenomena and discourse-level phenomena is made more clear. A single structure is computationally more ecient.

3.1 The Multi-Stack Data Structure In some instances, the single focus stack structure seems to pop o segments which will be needed later. An approach to this problem is to de ne a dierent data structure which allows some segments to be pushed on the stack at the same level. Figure 3.2 gives a pictorial representation rst of

1. 2. 3. 4. 5.

Mary: I think that handguns should not be banned. Mary: The constitution guarantees every citizen the right to Mary: and, anyway, I don't think that the streets will be any John: Look, the constitution does not guarantee that right; John: it only guarantees that states have the right to form National Guard.

bear arms, safer. militias like the

Figure 3.1: A Sample Discourse the single stack and its associated tree and then of the multi-layered stack and its associated tree

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CHAPTER 3. THE MULTI-STACK HIERARCHY

structure for the example dialogue given in Figure 3.1.

3.1. THE MULTI-STACK DATA STRUCTURE

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Single Stack Approach

Multi-Stack Approach

(Box Representation)

(Box Representation) S1 S2

S1 S2

S1 S2 S4

S1 S2 S4 S5

S5 S2 S3 S4 S4 S1 S1 S1 S1 S3 S3 S3 After S2 After S3 After S4 After S5 After S2 After S3 After S4 After S5 (Tree Representation) S1 S1 S1 S1 S2

S3

S4

S4 S5

S1 S2

(Tree Representation) S1 S1 S1 S2 S3 S2 S4

S3

S2

S3 S4

S5 After S2 After S3 After S4 After S5 After S2 After S3 After S4 After S5

Figure 3.2: A Comparison Between the Single Stack and the Multi-stack


22

The multi-stack is constructed in a way similar to how Reichman creates her context space hierarchy and how Cohen and Birnbaum construct their argument graphs. Like Grosz, Cohen, and Birnbaum will assume that there are only very primitive relationships between discourse segments like support and attack. A trace of how the multi-stack is created for the dialogue in Figure 3.1 proceeds as follows: Using this new data structure the stack (and tree) after processing Sentence 3 in Figure 3.1 would be as follows as both 2 and 3 are attacks of 1: Segment 1 Segment 2

Segment 3

Segment 1

Segment 2

Segment 3

and after Sentence 4 (as 4 is an attack on 2):

3.1. THE MULTI-STACK DATA STRUCTURE Segment 1 Segment 2 Segment 4

Segment 3 Segment 1

Segment 2

Segment 3

Segment 4 and after Sentence 5 (if Segment 5 is viewed as a support of Segment 4):

23


24 Segment 1 Segment 2 Segment 4 Segment 5 Segment 3 Segment 1

Segment 2

Segment 3

Segment 4 Segment 5

3.2 The Multi-Stack Approach and the Maintenance of Focus There are many questions to be answered about this new structure. Assuming each segment has its own history list and focus list [1], there must be new rules determining which history lists and focus lists are active at a given time in a multi-layered stack environment.

3.2.1 Discourse Segment Status Assignments

Following Reichman, the following status assignments can be given to a discourse segment: 1. Active 2. Controlling 3. Open 4. Precontrol 5. Closed Active discourse segments are those segments in which current utterances are being placed. A segment is given a Controlling status assignment if it has an active descendent. An Open discourse segment was previously active but not yet given closed status. A segment is given Precontrol

3.3. THE MULTI-STACK APPROACH AND DISCOURSE CONTROL

25

status if it has an Open descendent. A Closed segment is a segment which is believed completed. Since a segment may have several descendents, some open, some closed, some active, there may be some confusion over what status to give that segment. If a segment has an active descendent, it is controlling. If a segment has no active descendents, but it does have an open descendent, then it is precontrolling. If a segment has only closed descendents, it is considered open unless the segment, itself, has been closed. Unlike Reichman, the number of active segments is not limited to one; rather, the number of active segments may be less than or equal to the number of participants.

3.2.2 Focus Assignments

Reichman rules on the assignment of focus seem far too strict. Instead, all elements in a segment will have the same level of focus. Computationally, however, it may be useful to order the elements to speed the search. This ordering could take into account some of Reichman's principles.

3.2.3 Pronominal and Deictic Reference Rules

Similar to Reichman's rules, the following four rules limit the usage of pronominal and deictic references. Rule #1: Only elements in a currently active or controlling said context space may be referenced pronominally (A context space is given the status of said if the context space was introduced by an verbal utterance. A context space is given the status of unsaid if that context space is unspoken and was introduced through deduction/induction.). An exception occurs when there are two active context spaces. When adding a segment to one active context space, the other active context space is treated as open. Rule #2: Only elements in a currently active or controlling said context space may be referenced by a close deictic referring expression (here, this). Rule #3: Full de nite descriptions are needed to reference elements in a closed or unsaid context space. Rule #4: Only far deictic or full descriptive referring expressions can be used to refer to elements of an open or precontrol said context space. If there are two active context spaces, references from one active context space to another cannot be pronominal. Rather the other active context space is treated as open. Therefore, far deictic references may be used. Applications of these rules explain the usage of pronominal and deictic expressions. Note that the special case of parallel conversation is handled by having two active segments. Although the processing takes longer as both active segments must be inspected when interpreting an incoming segment, this lengthened processing times seems consistent with human comprehension (i.e., it seems to take longer for humans to process parallel conversations, too).

3.3 The Multi-Stack Approach and Discourse Control The arguing mechanism described here closely resembles the mechanism described by Birnbaum et al. In a debate, arguer A may state a belief S1 and then support it with three other beliefs or facts: S2, S3 and S4. If A's opponent, arguer B, does not believe S1, B must attack S1. In doing so, B must also undermine S2, S3, and S4. Thus an attack on S1 may involve the subgoals of attacking S2, S3, and S4. The multi-stack approach provides a data structure for representing that discourse. This data structure helps to constrain further discourse as will be seen in the following example.


26

3.3.1 The \Game" of Arguing

Each node in the following graph is a segment. Leftward-branching children of a node are supports of that node; rightward-branching children of a node are attacks on that node. Assume that the following has already occurred: 1. John has asserted S1. 2. John supports S1 with S2 and S3. 3. Bill attacks S1 with S4. 4. Bill then attacks S2 with S5 and S6. The tree structure for the debate so far would be: S1 S2

S3

S4

S5 S6 This structure now helps dictate what John's reply can be. He could believe that Bill's attack is sucient and concede S1. If not, he must now try to defend himself. There are several options available. John can choose one or more of the following: 1. John can give more supports of S1. 2. John can point out that Bill did not attack S3, thereby challenging Bill. If Bill does not respond to this challenge, John will have "won" S3. 3. John can give more supports to S2. 4. John can attack S5 and S6. If John does not attack S5 and S6, his support S2 will remain weakened and will not give very good support to S1. 5. John can attack S4. If he does not try to undermine S4, his claim S1 will be weakened. The structure of the debate so far determines John's possible moves. The debate is viewed as a game where a debater tries to "win" nodes. Winning a node occurs when that node's supports are won, and/or the opponent can not win the attacking nodes of that node. Viewing discourse as a game has been suggested before [4][5]. A debate itself is very much a verbal game. So unlike Reichman's ATN model of discourse structure, this approach has a much more complex model to control the ow of discourse.

3.3.2 A Trace of a Debate

In this section, a walk-through of how the debate given in Figure 1.1 (reprinted here as Figure 3.3) would be processed using the multi-stack architecture. For clarity, the speaker who utters a particular statement will be referred to as the generator while the listener will be referred to as the processor. Note that in a debate all participants will take both roles many times.


1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

John: Handguns should be banned. Mary: But with a gun a woman can defend herself against a rapist. John: There will be fewer murders if handguns are banned. Mary: You haven't addressed my argument. John: They have gone down in other countries after a strict handgun ban

in place. Mary: Look, if one comes in my home, I want to have a pistol handy. John: A gun could be turned against a homeowner. John: That criminal could turn your gun against you. Mary: Well, it's stupid to ban handguns. Mary: Criminals won't worry about the misdemeanor of having a banned John: They won't be able to get guns if there are none available to steal. Figure 3.3: A Sample Debate

27

has been put

item.


28

Sentence 1 The start of the debate begins with a creation of the top node. Like all subsequent segments, this node will contain information pertaining to that segment including such items as the logical meaning of that segment and elements of that segment which may be needed to process further segments. Its status is active 1 active

Sentence 2 Mary does not believe John's statement that handguns should be banned. Therefore,

Mary must somehow attack John's statements. Mary, as generator, searches her knowledge base to determine what would be an appropriate attack of 1. One attack of node 1 is the fact that handguns can be used for self-defense. A support of this fact is that women can use guns for self-defense against rapists. Mary responds with this argument. The processor of Sentence 2 will constrain its search of the possible meanings of Sentence 2 by looking to see whether Sentence 2 supports or attacks Sentence 1. How this relationship is determined will be discussed in Chapter 4. Once sentence 2 is processed as a support of the statement that handguns can be used for self-defense which is an attack of 1, it is pushed on top of segment a (the segment representing the unsaid support, handguns can be used for self-defense). It is given active status. 1 Controlling a Controlling(unsaid)

2 Active

Sentence 3 John, now as generator, wants to defend his original statement. He can now attack

node 2 or give supports for node 1. For whatever reason, perhaps because he does not have a good attack of node 2, John proceeds by giving support to node 1. In processing Sentence 3, the search begins by seeing how it relates to the active segment, 2. Since there is no obvious relation, the tree is traversed toward the root to see where the sentence ts. After some search, it is processed as a support of node 1. It is given active status.


29

1 Controlling a Controlling(unsaid)

3

Active

2 Active

Sentence 4 Mary sees that John has continued down another path in the debate tree without refuting her point about self-defense. She points this out to John. Sentence 4 does not obviously relate to node 3. The processor now looks to see what other nodes are active. Since node 2 is active, the my argument phrase is interpreted to refer to the argument presented there. Sentence 4 is not a new node as it is neither an attack nor a support; it is merely attached to node 2. 1 Controlling a Controlling(unsaid)

3

Active

2 ,4 Active

Sentence 5 Again John does not address Mary's argument and goes his own way down the path.

Here, John clearly is in a parallel conversation. Mary would rst try to process Sentence 5 as an attack of Node 2. However, after trying substitutions for they like rapists, women in Sentence 5, no likely interpretation can be found. The search then looks at other active nodes. Since node 3 is active, and the phrase Murders have gone down in other countries after a strict handgun ban has been put in place can easily be understood as a support of node 3, node 5 is created.


30

1 Controlling a

Controlling) (unsaid) Controlling 3

5

2 ,4 Active

Active

Sentence 6 Mary, using the attention grabbing Look, again attempts to get John to discuss her argument. Here she decides to defend node 2 by providing a support. John rst would try to process the sentence in some relation to node 4. As none is easily found, other active nodes are searched. Node 2 is active, so John's search moves to here. By substituting a rapist for one in Sentence 6 a plausible support relationship can be drawn for Node 2. 1 Controlling Controlling (unsaid)

a

Controlling Controlling 2 ,4

3

6

5 Active

Active


31

Sentence 7 John now decides to address Mary's argument. However, instead of directly attacking

node 6, John traverses up the tree and attacks the unsaid node a. In processing Sentence 7, Mary has a good deal of searching to do to nd where this statement ts with the rest of the debate. 1 Controlling a

Controlling (unsaid) Pre-Control

Controlling

3

5

2 ,4

Active 7

6 Open

Active

Sentence 8 John continues and adds some support to sentence 7 by giving an example. Here he

makes reference to the criminal Mary was supposed to be defending against in Sentence 6. This deictic reference is legal by Rule#4. However, the use of he to refer to the criminal mentioned in Sentence 6 would be illegal by Rule #1. Processing Sentence 8 is relatively straight-forward once the correct indexing of that criminal is found.


32

1 Controlling a

Controlling (unsaid) Pre-Control

Controlling 7

3

5 Open

2 ,4

6 Active

Controlling

8 Active

Sentence 9 Mary decides to try a new tack in her argument. She prefaces this by asserting

Sentence 9. Here she has clearly backed up the tree to the top node. By doing so, she closes o the spaces that she was involved in. Sentence 9 is not truly an attack on Node 1; it is more of a meta-argument move. 1 ,9 Controlling Controlling (unsaid) Pre-Control

PreControl

3

5

a 7 2 ,4

6 Open

Open

Controlling 8

Active


33

Sentence 10 Mary decides she wants to attack John's assertion that there will be fewer murders.

One way to do this is to undermine possible supports of that assertion. One support of that assertion is that criminals will not be able to get guns (node b in the following graph). An attack on node b is the assertion that a handgun ban could not be enforced (node c). A support of this fact is the assertion that criminals will ignore the ban since the severity of breaking the ban is not as great as the crimes they will commit. This statement is what is said by Mary. Processing is fairly straight-forward with the insertion of the unsaid nodes. 1 ,9 Controlling a

Controlling (unsaid) Pre-Control 3

Open 5

2 ,4 Closed b

6

Controlling

8 Closed

unsaid

7

Active

c unsaid

10 active

Sentence 11 Perhaps John cannot attack Mary's last statement. He traverses the tree toward

the node trying to nd nodes to attack (if they represent assertions against his opinion) or to support. John apparently decides he cannot attack the unsaid node representing the claim that a ban could not be enforced. Instead he argues that indeed criminals will not be able to get guns because there will be none available to steal.


34

1 ,9 Controlling a

Pre-Control unsaid Pre-Control

Closed

3

5

2 ,4

Open

unsaid

6

b 11

8 Closed c unsaid

active 10 active

Open

7 Pre-Control

Chapter 4

The debater System The debater system is an implementation of the multi-stack hierarchy in the processing of argumentative discourse. The implementation was carried out in prolog to take advantage of prolog parsing mechanisms as well as automated prolog search. The general design of the system followed the traditional computational linguistic division between syntax, semantics, and pragmatics. Figure 4.1 gives a general representation of how the system is constructed.

4.1 Parsing The processing of each sentence is accomplished by rst syntactically parsing each sentence using prolog grammars. debater uses approximately 100 grammar rules to encode the possible sentence structures. The parser uses a lexicon of over 800 words. Additional words can be recognized by constructions on the 800+ words in the lexicon. For example, simple plurals can be built out of singular nouns. Tenses of simple verbs can be constructed. Gerunds can be formed out of simple verbs. Possessives like the man's can also be understood.

35

CHAPTER 4. THE DEBATER SYSTEM

36

DIALOGUE RULES DIALOGUE MODEL ....R I INTERPRET - GENERATE STATEMENTS RESPONSE

M

SYNTACTIC PARSER (SYNTAX)

k

6

PARSER - CONVERT TO - EXTRACT LOGICAL SUBTREES FORM (SEMANTICS) (PRAGMATICS)

M

W

?. ?

STRUCTURED DATABASE

Figure 4.1: General Representation of the debater System

4.2. THE DATABASE

37

Once a sentence is parsed syntacticly, this syntactic representation (in the form of a parse tree) is then analyzed to produce a semantic representation. This semantic representation is more general than the syntactic representation. It only records essential details such as the verb, the agent, the patient, the recipient, and essential modi ers. Details such as whether the sentence is passive or active, or whether an adverb precedes or follows the verb are not recorded. If a semantic representation cannot be found, the sentence is given back to the syntactic parser to see if there are other viable representations. Once a semantic representation is found, the system must then try to gure out how this statement relates to other statements in the discourse. This process is a pragmatic interpretation. The system operates by checking if there is any relationship between the incoming segment and any active segments in the discourse tree. If one is found, the segment is added appropriately. However, if there is no relation to an active segment, the search continues up the tree toward the root. If no node is found in the current tree to which the incoming segment seems to be related, the possibility that the incoming segment is related to hidden nodes is explored. The database is searched to see what possible nodes the incoming segment modi es. Each of these nodes is then treated as an unsaid incoming segment, and an attempt is made to insert this unsaid, hidden node. This process is recursive, so there is the possibility of many hidden nodes being added. Once the hidden nodes are added, the original incoming segment may now be appropriately added to the discourse tree.

4.2 The Database

The database that debater uses is a static database which is used to help determine the relationship between segments. Keep in mind that the database is a separate entity from the dynamic structure built during the course of the argument.

4.2.1 The Problem of Determining Relationships Between Segments

Determining what relation one segment has to another is not trivial. For example, how could a computer determine that \the constitution guarantees all citizens the right to bear arms" is an attack on \Handguns should be banned"? The computer would have to have information that the constitution is a highly regarded document, that the constitution is considered to contain fundamental rights of citizens, that the right to bear arms can be interpreted to include handguns, and so on. An enormous amount of world knowledge is needed to be able to determine just the single relation. If an appropriate database and associated mechanisms could be constructed which could determine the relation of one segment to another, many of the problems of natural language processing would be solved.

4.2.2 The Argument Database

The debater database is very much like the database that many expert systems use. The database used in this system does not determine the relationship between segments by looking at the semantic meanings of the two segments. The database bypasses this complex process by having the relationships between all segments pre-de ned. In the debate database, an expert enters all possible supports and attacks on the claim being argued. In turn the supports and attacks for each support and each attack are entered into the database. Needless to say, this approach to the relationship problem has a severe limitation: if a user enters a segment which is not in the database, there is no way that the computer can determine

38


its relation to other segments in the database. Also, there is a signi cant problem in creating the database. The information \bottleneck" encountered in expert systems is similar. The database must be extensive and comprehensive. A bene t of this database is seen when the computer wants to attack or support a statement. All of the supports and attacks of that statement are found right in the database. If the user enters a statement that is somewhere in the database, its relationship to all the other segments in the database can be determined.

4.3 Discourse Rules The tree structure (or multi-stack structure) described earlier helps constrain the possible discourse responses. The possible responses given a particular debate tree structure was described informally in 3.3.1. Now a more formal description of the discourse grammar will be presented.

4.3.1 Verbosity of Response

The structured database provides a powerful mechanism for representing the verbosity of a particular response. A debater may respond to a challenge with a very deep response, a very broad response, or some combination of the two.

Depth of Response

Here is an intuitive idea of what is meant by the depth of a response: Depth 0: Handguns should be banned. (Claim, but no support given) Depth 1: Handguns should be banned. If handguns are banned then there will be less murders. (Claim, and one level of support). Depth 2: Handguns should be banned. If handguns are banned, then there will be less murders. Handguns are banned in Japan, and the murder rate there is extremely low as compared to the United States. (Claim, and two levels of support. There is a support of the claim and a support of the support). Depth 3: Handguns should be banned. If handguns are banned, then there will be less murders. Handguns are banned in Japan, and the murder rate there is extremely low as compared to the United States. Some may argue that the Constitution gives everyone the right to own a handgun, but that is not true. The Constitution only gives states the right to form a militia.(Claim, two levels of support, an attack on an attack on the claim. There is a support of the claim, a support of the support, and the person putting forth the claim tries to defeat in advance an argument that may be put forth by the opponent). The more the depth of a response means the more support will be given to the claim. A debater can determine what depth to carry his response based on the circumstances. If the debate is informal and casual or the opponent is a novice debater, there is no reason to respond initially with great depth. On the other hand, if the opponent is a skilled debater, perhaps a higher depth is more appropriate. The database (as formalized by a tree diagram) has a mathematical de nition of depth, the number of levels from the root of the tree. This mathematical de nition corresponds roughly to the depth of a response.

4.3. DISCOURSE RULES

39

Breadth of Response There is also another notion of level of support/attack. This involves the breadth of support/attack rather than depth. A debater may have many supports of a particular claim, but he may only want to specify one or two at any one time. Examining the tree structure of the database, the breadth of a response would involve descending one level of depth and responding with all the supporting/attacking nodes at that level.

A Combination of Depth and Breadth of Response A particularly good strategy may be to choose which support/attack is the best (using a breadth rst search) and then respond with that node and perhaps a depth- rst support (of some length) of that node.

4.3.2 Some Debate Heuristics

Asserting the Debate Topic

In asserting the debate topic (the fact at the top of the database, for example, \handguns should be banned".), only one choice needs to be made: how many levels of support should be given. In general, it may be a good strategy to give only the support that is needed to defend against an opponent's attacks. The more information one supplies to one's opponent, the more the opponent can attack.

Attacking a Claim If a claim goes unattacked, then that claim is automatically \won" by its supporter. Therefore, if possible, a debater should always attack a claim made by the opponent. What the level of attack should be is somewhat arbitrary. If the opponent gives support to his claim, it is important to undermine that support. It is not necessary to attack all of an opponent's supports at once. A particularly weak support could be chosen rst, and then, upon defeating that support, turn to attack the other supports.

Supporting an Attacked Claim

If a claim is attacked, it is important to protect that claim. Attack any attacks of the claim. Support any of the supports that have been attacked. If an opponent fails to attack a claim or support, point that out. If the opponent does not respond, i.e., does not attack it, then that claim or support has been won.

Conceding a Claim If you have lost all your attacks on an opponent's claim (or have not been able to successfully defend a claim of your own), you must concede that claim. You can either concede verbally (\I concede your point that ....") or you can go on to something else. It cannot be assumed that your opponent will miss your concession if you don't mention it. You have \lost" that claim. Losing a claim has an eect on the whole structure. If the lost claim is a support of another claim, the supported claim is weakened. Further support of that claim is necessary (if it has not already been given). Similarly, if the lost claim is an attack on a claim, the attacked claim is not weakened; further attacks of that claim (or attacks on its supports) are needed.


40

Demanding Supports If an opponent makes a claim with no support, ask for support of that claim. It may be best to do this before attacking that claim. Asking for support is equivalent to just denying that claim. However, a plain denial of that claim (without accompanying attacks) is likely to be met with a counter question demanding attacks.

Demanding Attacks If an opponent denies your claim but does not back it up, ask for attacks of your claim. A simple denial of your claim does not amount to much of an attack.

Winning the Debate Most debates are not de nitively won or lost. Rather there is a more subjective determination of winning and losing. If the database is adequate and evenly balanced for both sides of an argument, there is the possibility that neither side wins. With controversial debates over gun control and abortion, it is often the case that neither side can present a completely convincing argument. What generally happens is that one side wins some points, the other side wins other points. The debater system re ects this philosophy and tries to win all the nodes that it can. It does not give up if it does not win a particular node; it goes on and tries to win other nodes. The only time it quits is when it runs out of things to say because of a limit in its database.

4.4

debater

in Action

Appendix A gives a few example debates with the computer debating a human opponent (A.1, A.2) and the computer debating itself (A.3). These examples show that the debater system can argue competently and cohesively when judged by human standards. Response times are short; feedback from the computer is often under three seconds and rarely takes more than ten seconds.

Chapter 5

Future Research The multi-stack hierarchy satisfactorily provides a structure to process higher level argumentative discourse as well as successfully accounting for some unusual pronominal and deictic references. The debater system supports the following research directions: The multi-stack segment hierarchy can be used as a representation of complex discourse interactions which can not be handled using a single stack scheme or an ATN grammar. Other discourse types may be suitable to a multi-stack representation such as problem-solving tasks which generally rely on a hierarchy of goals. The multi-stack hierarchy can support an arbitrarily large number of discourse relations. The DEBATER system is primarily concerned with support and attack relations. However, there is no inherent reason why it could not support other relations. The multi-stack hierarchy can aid in realizing pronominal and deictic reference by providing a mechanism to track focus. Schemes for ordering elements available for pronominal and deictic reference need to be explored. The hierarchy of segments and of the database can help give expectations to the natural language parser. The use of expectations in dialogue processing has been shown to signi cantly increase the performance of natural language processors [8]. Discovering a mechanism for determining the relationship between two segments would greatly enhance the performance of this system which relies on a static and restricting database. The multi-stack hierarchy would help restrict the number of possible relationships that two segments could hold.

41

42

CHAPTER 5. FUTURE RESEARCH

Chapter 6

Conclusions The multi-stack mechanism described in this paper aids in solving two problems: The multi-stack segment hierarchy can be used as a representation of complex discourse interactions which can not be handled using a single stack scheme or an ATN grammar. The multi-stack hierarchy can aid in realizing pronominal and deictic reference by providing a mechanism to track focus. On the higher discourse level, the multi-stack hierarchy provides a framework for processing incoming segments and determining their meaning in context. The hierarchy also is useful in participating in the discourse as it provides guidelines for how to proceed next. The search up and down the tree hierarchy seems to simulate well how people conduct a debate. On the surface linguistic level, the multi-stack hierarchy accounts for pronominal and deictic references during a debate even if that debate involves parallel conversations and multiple argument utterances. The fact that a structure which is used to represent the intentional structure of a discourse can be used to account for certain syntactic phenomenon stresses the interrelation between the two. Clearly, syntactic issues cannot be addressed independently of intentional structure.

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44

CHAPTER 6. CONCLUSIONS

Bibliography [1] James Allen. Natural Language Understanding. The Benjamin/Cummings Publishing Company, Inc., Reading,Massachusetts, 1987. [2] Lawrence Birnbaum. Argument molecules: A functional representation of argument structure. Proceedings of American Association for Arti cial Intelligence Conference, pages 63{65, 1983. [3] Lawrence Birnbaum, Margot Flowers, and Rod McGuire. Towards an AI model of argumentation. Proceedings of American Association for Arti cial Intelligence Conference, pages 313{315, 1980. [4] Lauri Carlson. Dialogue Games { An Approach to Discourse Analysis. D. Reidel Publishing Company, Boston, 1983. [5] Lauri Carlson. WELL in Dialogue Games. John Benjamins Publishing Company, Philadelphia, 1984. [6] David D. Clarke. Language and Action { A Structural Model of Behavior. Pergamon Press, New York, 1983. [7] Robin Cohen. Analyzing the structure of argumentative discourse. Computational Linguistics, 13(1-2):11{24, January-June 1987. [8] Pamela Kennedy Fink. The Acquistion and Use of Dialogue Expectation in Speech Recognition. PhD thesis, Department of Computer Science, Duke University, December 1983. [9] Robert E. Frederking. Integrated natural language dialogue: A computational model. Kluwer Academic Publishers, Boston, 1988. [10] Barbara Grosz. Discourse analysis. In D. Walker, editor, Understanding Spoken Language, pages 235{268. Elsevier North-Holland, New York, NY, 1978. [11] Barbara Grosz and C. Sidner. Attention, intentions, and the structure of discourse. Computational Linguistics, 12(3):175{204, 1986. [12] Robert E. Longacre. The Grammar of Discourse. Plenum Press, New York, 1983. [13] William C. Mann and Sandra A. Thompson. Rhetorical structure theory: A theory of text organization. Technical Report ISI/RS-87-190, Information Sciences Institute Reprint Series, Marina del Rey, California, 1987. Reprinted from The Structure of Discourse. [14] Kathleen R. McKeown. Text Generation { Using discourse strategies and focus constraints to generate natural language text. Cambridge University Press, New York, 1985. 45

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BIBLIOGRAPHY

[15] Kathleen R. McKeown. Discourse strategies for generating natural-language text. In Bonnie Lynn Webber Barbara J. Grosz, Karen Sparck Jones, editor, Readings in Natural Language Processing, pages 479{499. Morgan Kaufmann Publishers, Inc., Los Altos, California, 1986. [16] Janos S. Peto , editor. Text and Discourse Constitution. Walter de Gruyter, New York, 1988. [17] Rachel Reichman. Getting Computers to Talk Like You and Me { Discourse Context, Focus, and Semantics (An ATN Model). The MIT Press, Cambridge, 1985. [18] Rachel Reichman and R. Adar. Extended person-machine interface. Arti cial Intelligence, 22(2):157{218, 1984. [19] Michael Stubbs. Discourse Analysis { The Sociolinguistic Analysis of Natural Language. The University of Chicago Press, Chicago, 1983. [20] Teun A. van Dijk and Walter Kintsch. Strategies of Discourse Comprehension. Academic Press, New York, 1983.

Appendix A

Sample Debates A.1 Debate Example 1 The rst two dialogues are actual dialogues between the computer and a user. The rst dialogue has the computer taking the position that handguns should be banned. The second dialogue has the computer taking the opposite position. Bracketed annotations accompany the dialogue to explain various sections. debater is implemented in Quintus prolog Release 2.4.2 running on a Sun-4, SunOS 4.1. COMPUTER:

HANDGUNS SHOULD BE BANNED.

USER: |: handguns should be legal. the constitution guarantees every citizen the right to bear arms.# COMPUTER: [An attack of the user's attack.] I DO NOT BELIEVE THAT THE CONSTITUTION GUARANTEES THE RIGHT OF EVERYONE TO BEAR ARMS. THE CONSTITUTION ONLY GUARANTEES THE RIGHTS OF STATES TO CREATE A MILITIA. USER: [The user proceeds by giving another argument.] that is your opinion. anyway. police can not protect all citizens.# COMPUTER: YOU HAVE NOT ADDRESSED MY STATEMENT THAT THE CONSTITUTION ONLY GUARANTEES THE RIGHTS OF STATES TO CREATE A MILITIA. USER: [The user now gives a support of the earlier argument.] the supreme court supports the right to bear arms interpretation of the 2nd amendment.# COMPUTER: [The computer does not have a response to the previous argument so it goes back up the tree to defend the original argument.] HANDGUNS SHOULD BE BANNED. IF HANDGUNS ARE BANNED THERE WILL BE FEWER MURDERS AND OTHER VIOLENT CRIME. IF HANDGUNS ARE

47

APPENDIX A. SAMPLE DEBATES

48

BANNED THERE WILL BE FEWER SUICIDES. IF HANDGUNS ARE BANNED THERE WILL BE FEWER ACCIDENTAL DEATHS. USER: [The user responds by attacking the unstated support that criminals will not be able to get guns if they are banned.] there will not be fewer murders. criminals obtain 80 percent of their guns by illegal means.# COMPUTER: I DO NOT BELIEVE THAT CRIMINALS WILL STILL BE ABLE TO GET GUNS BUT HONEST CITIZENS WILL NOT BE ABLE TO GET GUNS. IF THERE ARE NO GUNS TO STEAL CRIMINALS CAN NOT ILLEGALLY OBTAIN GUNS. USER:

guns are easily made.

any metal shop can make a gun.#

COMPUTER: [Here again the computer has no direct response. A search is made back up the tree to support previous arguments.] IF HANDGUNS ARE BANNED THERE WILL BE FEWER MURDERS AND OTHER VIOLENT CRIME. HANDGUNS ACCOUNT FOR MOST OF THE MURDERS IN THE US. STRICT GUN CONTROL IN OTHER COUNTRIES HAVE RESULTED IN FAR LOWER MURDER RATES. THERE WILL BE FEWER MURDERS BECAUSE A HANDGUN WILL NOT BE AVAILABLE TO ESCALATE THE VIOLENCE OF THE SITUATION. USER: in morton grove handguns were banned and the crime rate rose 33 percent.# COMPUTER: STRICT GUN CONTROL IN OTHER COUNTRIES HAVE RESULTED IN FAR LOWER MURDER RATES. IN GREAT BRITAIN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN JAPAN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN CANADA THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN SWEEDEN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. USER: the crime rate rose in great britain after the gun ban. japan has a different social structure.# COMPUTER: IN GREAT BRITAIN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN ENGLAND APPROXIMATELY 2 HOMICIDES OCCUR PER 100000 PERSONS PER YEAR. USER:

the crime rate in great britain is growing.#

A.1. DEBATE EXAMPLE 1 COMPUTER: YOU HAVE NOT ADDRESSED MY STATEMENT THAT IN ENGLAND APPROXIMATELY 2 HOMICIDES OCCUR PER 100000 PERSONS PER YEAR. USER: strict gun control in the us has not resulted in lower murder rates.# COMPUTER: IN JAPAN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN JAPAN APPROXIMATELY 1 HOMICIDE OCCURS PER 100000 PERSONS PER YEAR. USER: [Notice the spelling error ``resulteed'' is caught. Simple spelling correction eases the human-computer interface.] you have not addressed my argument. strict gun control in the us has not resulteed in lower murder rates.# COMPUTER: [Here is a ``parallel conversation'' where each speaker has an agenda.] YOU HAVE NOT ADDRESSED MY STATEMENT THAT HANDGUNS ACCOUNT FOR MOST OF THE MURDERS IN THE US. USER: 1000000 crimes are deterred by citizens with guns per year.# COMPUTER: HANDGUNS ACCOUNT FOR MOST OF THE MURDERS IN THE US. A MAJORITY OF VIOLENT CRIMES IN THE US INVOLVE HANDGUNS. USER:

armed citizens can protect democracy.#

COMPUTER: A MAJORITY OF VIOLENT CRIMES IN THE US INVOLVE HANDGUNS. IN 1986 59 PERCENT OF ALL MURDERS WERE COMMITTED WITH GUNS. USER: a government could not oppress its citizens if all citizens owned guns.# COMPUTER: THERE WILL BE FEWER MURDERS BECAUSE A HANDGUN WILL NOT BE AVAILABLE TO ESCALATE THE VIOLENCE OF THE SITUATION. A PERSON MAY KILL OUT OF ANGER WITH AN EASILY AVAILABLE HANDGUN. MANY MURDERS ARE COMMITTED BY CLOSE FAMILY MEMBERS. USER:

[No response.] #

COMPUTER: YOU HAVE NOT ADDRESSED MY STATEMENT THAT A PERSON MAY KILL OUT OF ANGER WITH

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50 AN EASILY AVAILABLE HANDGUN. USER:

#

COMPUTER: [The computer gives further support of the main argument.] IF HANDGUNS ARE BANNED THERE WILL BE FEWER SUICIDES. MANY SUICIDES RESULT FROM SUDDEN DEPRESSION. THE EASY ACCESS TO HANDGUNS ALLOWS THE DEPRESSED PERSON TO QUICKLY TAKE HIS LIFE. WITHOUT A HANDGUN THAT PERSON MIGHT NOT COMMIT SUICIDE. USER:

there are many other ways to commit suicide.#

COMPUTER: YOU HAVE NOT ADDRESSED MY STATEMENT THAT MANY SUICIDES RESULT FROM SUDDEN DEPRESSION. USER:

#

COMPUTER: YOU HAVE NOT ADDRESSED MY STATEMENT THAT THE EASY ACCESS TO HANDGUNS ALLOWS THE DEPRESSED PERSON TO QUICKLY TAKE HIS LIFE. USER:

#

COMPUTER: IF HANDGUNS ARE BANNED THERE WILL BE FEWER ACCIDENTAL DEATHS. ALL OF THE ACCIDENTAL HANDGUN DEATHS OF THE CHILDREN AND THE ADULTS WOULD BE AVOIDED. 800 PEOPLE DIE FROM GUN ACCIDENTS PER YEAR. USER: accidental deaths would be reduced if people got proper training.# COMPUTER: YOU HAVE NOT ADDRESSED MY STATEMENT THAT 800 PEOPLE DIE FROM GUN ACCIDENTS PER YEAR. USER:

800 accidents out of 200 million guns is not bad.#

COMPUTER: [Here the computer has run out of supports and counter-arguments. YOU WIN. I HAVE NO FURTHER ARGUMENTS.

A.2 Debate Example 2 In this sample debate, the computer takes pro-gun side of the argument. COMPUTER:

I DO NOT BELIEVE THAT HANDGUNS SHOULD BE

A.2. DEBATE EXAMPLE 2 BANNED. USER: |: if handguns are banned fewer murders will be committed.# COMPUTER: I DO NOT BELIEVE THAT IF HANDGUNS ARE BANNED THERE WILL BE FEWER MURDERS AND OTHER VIOLENT CRIME. ARMED CITIZENS CAN PROTECT THEMSELVES AGAINST VIOLENT CRIME. CRIMINALS WILL STILL BE ABLE TO GET GUNS BUT HONEST CITIZENS WILL NOT BE ABLE TO GET GUNS. CRIMINALS WILL CHOOSE OTHER AVAILABLE WEAPONS. USER: |: in other countries gun control has resulted in lower murder rates. in japan there are far fewer murders than in the us.# COMPUTER: I DO NOT BELIEVE THAT STRICT GUN CONTROL IN OTHER COUNTRIES HAVE RESULTED IN FAR LOWER MURDER RATES. IN SOME COUNTRIES GUNS CAN BE FOUND IN ALMOST EVERY HOUSEHOLD YET CRIME IN THESE COUNTRIES IS MUCH LESS THAN THAT OF THE US. STRICT GUN CONTROL IN THE US HAS NOT RESULTED IN FAR LOWER MURDER RATES. STRICT GUN CONTROL IN SOME COUNTRIES HAS NOT RESULTED IN FAR LOWER MURDER RATES. USER:

|: only small areas in the us have strict gun control.#

COMPUTER: [Although the computer can not directly attack the previous argument, it can give further support of its own argument.] STRICT GUN CONTROL IN THE US HAS NOT RESULTED IN FAR LOWER MURDER RATES. MORTON GROVE BANNED HANDGUNS AND THE CRIME RATE ESCALATED 33 PERCENT. WASHINGTON DC HAS A HIGH MURDER RATE AND WASHINGTON DC HAS VERY STRICT GUN CONTROL. NEW YORK CITY HAS A HIGH MURDER RATE AND NEW YORK CITY HAS VERY STRICT GUN CONTROL. USER: |: in great britain there are fewer murders. in england about 2 homicides per million persons per years.# COMPUTER: I DO NOT BELIEVE THAT IN GREAT BRITAIN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. THE MURDER RATE IN GREAT BRITAIN ESCALATED AFTER THE STRICT GUN LAWS. USER:

|: in canada ther are fewer murders than in the us.#

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52

COMPUTER: YOU HAVE NOT ADDRESSED MY STATEMENT THAT THE MURDER RATE IN GREAT BRITAIN ESCALATED AFTER THE STRICT GUN LAWS. USER:

|: #.

COMPUTER: YOU HAVE NOT ADDRESSED MY STATEMENT THAT IN SOME COUNTRIES GUNS CAN BE FOUND IN ALMOST EVERY HOUSEHOLD YET CRIME IN THESE COUNTRIES IS MUCH LOWER THAN THAT OF THE US. USER: |: |: there are not many countries with many guns and a low crime rate.# COMPUTER: IN SOME COUNTRIES GUNS CAN BE FOUND IN ALMOST EVERY HOUSEHOLD YET CRIME IN THESE COUNTRIES IS MUCH LOWER THAN THAT OF THE US. IN SWITZERLAND ALL MALE CITIZENS UPON SERVING IN THE ARMY KEEP THEIR AUTOMATIC WEAPONS. THE SWISS CRIME RATE IS VERY LOW. IN ISRAEL ALMOST ALL HOUSEHOLDS HAVE WEAPONS AT THEIR DISPOSAL. THE CRIME RATE IN ISRAEL IS LOWER THAN THE US. USER: |: there will be fewer murders because handguns escalte the violence of a situation.# COMPUTER: I DO NOT BELIEVE THAT THERE WILL BE FEWER MURDERS BECAUSE A HANDGUN WILL NOT BE AVAILABLE TO ESCALATE THE VIOLENCE OF THE SITUATION. PEOPLE CAN KILL WITHOUT HANDGUNS.

ANGRY

USER: |: it is easier to kill someone with a gun than with a knife.# COMPUTER: I DO NOT BELIEVE THAT IT IS EASIER TO KILL SOMEONE WITH THE DISTANCE OF A GUN. THE TYPICAL DISTANCE FROM A MURDERER TO A VICTIM IS 7 FEET. USER:

|: if handguns are banned there will be fewer suicides.#

COMPUTER: I DO NOT BELIEVE THAT IF HANDGUNS ARE BANNED THERE WILL BE FEWER SUICIDES. PEOPLE WILL CHOOSE OTHER MEANS TO COMMIT SUICIDE. USER: |: #

|: good bye....

A.3. DEBATE EXAMPLE 3

53

A.3 Debate Example 3 In this sample debate, the computer debates itself. "COMPUTER1" takes the anti-gun side and "COMPUTER2" takes the pro-gun side. COMPUTER1:

HANDGUNS SHOULD BE BANNED.

COMPUTER2: i do not believe that handguns should be banned. armed citizens can protect their families and their property from criminals. the constitution guarantees the right of everyone to bear arms. armed citizens can protect democracy. COMPUTER1: I DO NOT BELIEVE THAT ARMED CITIZENS CAN PROTECT THEIR FAMILIES AND THEIR PROPERTY FROM CRIMINALS. NO RESISTANCE IS BETTER THAN ARMED RESISTANCE. VIOLENCE CAUSES MORE VIOLENCE. COMPUTER2: i do not believe that no resistance is better than armed resistance. a person is less likely to be killed if that person deters the criminal with a gun. COMPUTER1: NO RESISTANCE IS BETTER THAN ARMED RESISTANCE. CRIMINAL MAY TURN THE WEAPON AGAINST THE VICTIM.

THE

COMPUTER2: armed citizens can protect their families and their property from criminals. police can not protect all citizens. 1000000 crimes are deterred per year by armed citizens. women with handguns can defend themselves from physically stronger attackers. COMPUTER1: I DO NOT BELIEVE THAT THE CONSTITUTION GUARANTEES THE RIGHT OF EVERYONE TO BEAR ARMS. THE CONSTITUTION ONLY GUARANTEES THE RIGHTS OF STATES TO CREATE A MILITIA. COMPUTER2: you have not addressed my statement that police can not protect all citizens. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT THE CONSTITUTION ONLY GUARANTEES THE RIGHTS OF STATES TO CREATE A MILITIA. COMPUTER2: you have not addressed my statement that 1000000 crimes are deterred per year by armed

54


citizens. COMPUTER1: I DO NOT BELIEVE THAT ARMED CITIZENS CAN PROTECT DEMOCRACY. CITIZENS WITH HANDGUNS CAN NOT DEFEAT A STRONG ARMY. THE US IS A DEMOCRACY. COMPUTER2: i do not believe that the us is a democracy. the us could become a tyranny if citizens do not defend their rights. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT CITIZENS WITH HANDGUNS CAN NOT DEFEAT A STRONG ARMY. COMPUTER2: you have not addressed my statement that the us could become a tyranny if citizens do not defend their rights. COMPUTER1: HANDGUNS SHOULD BE BANNED. IF HANDGUNS ARE BANNED THERE WILL BE FEWER MURDERS AND OTHER VIOLENT CRIME. IF HANDGUNS ARE BANNED THERE WILL BE FEWER SUICIDES. IF HANDGUNS ARE BANNED THERE WILL BE FEWER ACCIDENTAL DEATHS. COMPUTER2: i do not believe that if handguns are banned there will be fewer murders and other violent crime. armed citizens can protect themselves against violent crime. criminals will still be able to get guns but honest citizens will not be able to get guns. criminals will choose other available weapons. COMPUTER1: I DO NOT BELIEVE THAT CRIMINALS WILL STILL BE ABLE TO GET GUNS BUT HONEST CITIZENS WILL NOT BE ABLE TO GET GUNS. IF THERE ARE NO GUNS TO STEAL CRIMINALS CAN NOT ILLEGALLY OBTAIN GUNS. COMPUTER2: i do not believe that if there are no guns to steal criminals can not illegally obtain guns. guns can be made. guns can be smuggled. COMPUTER1: I DO NOT BELIEVE THAT CRIMINALS WILL CHOOSE OTHER AVAILABLE WEAPONS. A CRIMINAL WOULD BE LESS LIKELY TO KILL SOMEONE WITH A KNIFE THAN WITH A GUN. KNIVES AND FISTS ARE LESS LETHAL THAN HANDGUNS. COMPUTER2: i do not believe that a criminal would be less likely to kill someone with a

A.3. DEBATE EXAMPLE 3

55

knife than with a gun. in the northeast 24 percent of murders are committed with knives. COMPUTER1: A CRIMINAL WOULD BE LESS LIKELY TO KILL SOMEONE WITH A KNIFE THAN WITH A GUN. IT IS EASIER TO KILL SOMEONE WITH THE DISTANCE OF A GUN. GUNS ARE MORE LETHAL. COMPUTER2: i do not believe that it is easier to kill someone with the distance of a gun. the typical distance from a murderer to a victim is 7 feet. COMPUTER1: IT IS EASIER TO KILL SOMEONE WITH THE DISTANCE OF A GUN. THE MURDER IS MADE LESS PERSONAL. COMPUTER2: you have not addressed my statement that the typical distance from a murderer to a victim is 7 feet. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT THE MURDER IS MADE LESS PERSONAL. COMPUTER2: you have not addressed my statement that guns can be made. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT KNIVES AND FISTS ARE LESS LETHAL THAN HANDGUNS. COMPUTER2: criminals will still be able to get guns but honest citizens will not be able to get guns. criminals will not worry about misdemeanors. guns are easily made. a gun ban could not be enforced. criminals illegally obtain 80 percent of their guns. many guns are smuggled into the country. COMPUTER1: I DO NOT BELIEVE THAT GUNS ARE EASILY MADE. MANY CRIMINALS WILL NOT HAVE ACCESS TO HOMEMADE GUNS. COMPUTER2: gun.

guns are easily made.

any metal shop can make a

COMPUTER1: I DO NOT BELIEVE THAT A GUN BAN COULD NOT BE ENFORCED. MOST HANDGUNS WOULD BE CONFISCATED. COMPUTER2: a gun ban could not be enforced. too many people own handguns. many people will not return their guns.

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criminals will not worry about a gun ban. COMPUTER1: IF HANDGUNS ARE BANNED THERE WILL BE FEWER MURDERS AND OTHER VIOLENT CRIME. HANDGUNS ACCOUNT FOR MOST OF THE MURDERS IN THE US. STRICT GUN CONTROL IN OTHER COUNTRIES HAVE RESULTED IN FAR LOWER MURDER RATES. THERE WILL BE FEWER MURDERS BECAUSE A HANDGUN WILL NOT BE AVAILABLE TO ESCALATE THE VIOLENCE OF THE SITUATION. COMPUTER2: i do not believe that handguns account for most of the murders in the us. criminals will choose other available weapons. COMPUTER1: I DO NOT BELIEVE THAT CRIMINALS WILL CHOOSE OTHER AVAILABLE WEAPONS. IT IS HARDER TO KILL WITH A KNIFE. IT IS HARDER TO KILL WITH FISTS. COMPUTER2: criminals will choose other available weapons. shotguns and rifles will still be available for use. in the northeast 24 percent of murders are committed with knives. in the northeast 12 percent of murders are committed with fists. knives will still be available for use. fists will still be available for use. COMPUTER1: I DO NOT BELIEVE THAT SHOTGUNS AND RIFLES WILL STILL BE AVAILABLE FOR USE. IT IS MUCH HARDER TO CONCEAL A RIFLE OR A SHOTGUN. COMPUTER2: shotguns and rifles will still be available for use. in fact shotguns and rifles are used in many crimes. COMPUTER1: I DO NOT BELIEVE THAT IN FACT SHOTGUNS AND RIFLES ARE USED IN MANY CRIMES. SHOTGUNS AND RIFLES ARE HARDER TO CONCEAL THAN PISTOLS. COMPUTER2: i do not believe that strict gun control in other countries have resulted in far lower murder rates. in some countries guns can be found in almost every household yet crime in these countries is much less than that of the us. strict gun control in the us has not resulted in far lower murder rates. strict gun control in some countries has not resulted in far lower murder rates. COMPUTER1: I DO NOT BELIEVE THAT IN SOME COUNTRIES GUNS CAN BE FOUND IN ALMOST EVERY HOUSEHOLD

A.3. DEBATE EXAMPLE 3 YET CRIME IN THESE COUNTRIES IS MUCH LESS THAN THAT OF THE US. THERE ARE NOT MANY COUNTRIES WITH MANY GUNS AND A LOW CRIME RATE. COMPUTER2: in some countries guns can be found in almost every household yet crime in these countries is much less than that of the us. in switzerland all male citizens upon serving in the army keep their automatic weapons. the swiss crime rate is very low. in israel almost all households have weapons at their disposal. the crime rate in israel is lower than the us. COMPUTER1: I DO NOT BELIEVE THAT IN SWITZERLAND ALL MALE CITIZENS UPON SERVING IN THE ARMY KEEP THEIR AUTOMATIC WEAPONS. AMMUNITION IS STORED IN SEALED CONTAINERS. IT IS ILLEGAL TO BREAK THE SEAL. COMPUTER2: i do not believe that ammunition is stored in sealed containers. ammunition is easily available in stores in switzerland. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT IT IS ILLEGAL TO BREAK THE SEAL. COMPUTER2: in switzerland all male citizens upon serving in the army keep their automatic weapons. reservists keep their weapons at home. COMPUTER1: I DO NOT BELIEVE THAT STRICT GUN CONTROL IN THE US HAS NOT RESULTED IN FAR LOWER MURDER RATES. GUN CONTROL IN THE US IS LIMITED TO SMALL AREAS. COMPUTER2: you have not addressed my statement that in israel almost all households have weapons at their disposal. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT GUN CONTROL IN THE US IS LIMITED TO SMALL AREAS. COMPUTER2: you have not addressed my statement that the crime rate in israel is lower than the us. COMPUTER1:

I DO NOT BELIEVE THAT STRICT GUN CONTROL

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58

IN SOME COUNTRIES HAS NOT RESULTED IN FAR LOWER MURDER RATES. THERE ARE NOT MANY COUNTRIES WITH STRICT GUN CONTROL AND A HIGH CRIME RATE. COMPUTER2: the swiss crime rate is very low. in switzerland is lower than the murder rate in great britain. COMPUTER1: A SHOTGUN.

the murder rate

IT IS MUCH HARDER TO CONCEAL A RIFLE OR RIFLES AND SHOTGUNS ARE LARGER THAN PISTOLS.

COMPUTER2: strict gun control in the us has not resulted in far lower murder rates. morton grove banned handguns and the crime rate escalated 33 percent. washington dc has a high murder rate and washington dc has very strict gun control. new york city has a high murder rate and new york city has very strict gun control. COMPUTER1: HANDGUNS ACCOUNT FOR MOST OF THE MURDERS IN THE US. A MAJORITY OF VIOLENT CRIMES IN THE US INVOLVE HANDGUNS. COMPUTER2: you have not addressed my statement that morton grove banned handguns and the crime rate escalated 33 percent. COMPUTER1: A MAJORITY OF VIOLENT CRIMES IN THE US INVOLVE HANDGUNS. IN 1986 59 PERCENT OF ALL MURDERS WERE COMMITTED WITH GUNS. COMPUTER2: you have not addressed my statement that washington dc has a high murder rate and washington dc has very strict gun control. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT IN 1986 59 PERCENT OF ALL MURDERS WERE COMMITTED WITH GUNS. COMPUTER2: you have not addressed my statement that new york city has a high murder rate and new york city has very strict gun control. COMPUTER1: STRICT GUN CONTROL IN OTHER COUNTRIES HAVE RESULTED IN FAR LOWER MURDER RATES. IN GREAT BRITAIN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN JAPAN THERE ARE FAR FEWER

A.3. DEBATE EXAMPLE 3 MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN CANADA THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN SWEEDEN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. COMPUTER2: i do not believe that in great britain there are far fewer murders and other violent crimes than in the us. the murder rate in great britain escalated after the strict gun laws. COMPUTER1: IN GREAT BRITAIN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN ENGLAND APPROXIMATELY 2 HOMICIDES OCCUR PER 100000 PERSONS PER YEAR. COMPUTER2: you have not addressed my statement that the murder rate in great britain escalated after the strict gun laws. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT IN SWEEDEN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. COMPUTER2: i do not believe that in japan there are far fewer murders and other violent crimes than in the us. japan has a completely different social system. COMPUTER1: IN JAPAN THERE ARE FAR FEWER MURDERS AND OTHER VIOLENT CRIMES THAN IN THE US. IN JAPAN APPROXIMATELY 1 HOMICIDE OCCURS PER 100000 PERSONS PER YEAR. COMPUTER2: i do not believe that in canada there are far fewer murders and other violent crimes than in the us. strict handgun control in canada has not changed the murder rate. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT IN JAPAN APPROXIMATELY 1 HOMICIDE OCCURS PER 100000 PERSONS PER YEAR. COMPUTER2: strict gun control in some countries has not resulted in far lower murder rates. mexico has a strict ban on private firearms but mexico has a high murder rate. COMPUTER1:

THERE WILL BE FEWER MURDERS BECAUSE A HANDGUN WILL

59

60


NOT BE AVAILABLE TO ESCALATE THE VIOLENCE OF THE SITUATION. A PERSON MAY KILL OUT OF ANGER WITH AN EASILY AVAILABLE HANDGUN. MANY MURDERS ARE COMMITTED BY CLOSE FAMILY MEMBERS. COMPUTER2: i do not believe that there will be fewer murders because a handgun will not be available to escalate the violence of the situation. people can kill without handguns.

angry

COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT A PERSON MAY KILL OUT OF ANGER WITH AN EASILY AVAILABLE HANDGUN. COMPUTER2: you have not addressed my statement that criminals illegally obtain 80 percent of their guns. COMPUTER1: YOU HAVE NOT ADDRESSED MY STATEMENT THAT IF HANDGUNS ARE BANNED THERE WILL BE FEWER SUICIDES. COMPUTER2: you have not addressed my statement that many guns are smuggled into the country. COMPUTER1: IF HANDGUNS ARE BANNED THERE WILL BE FEWER SUICIDES. MANY SUICIDES RESULT FROM SUDDEN DEPRESSION. THE EASY ACCESS TO HANDGUNS ALLOWS THE DEPRESSED PERSON TO QUICKLY TAKE HIS LIFE. WITHOUT A HANDGUN THAT PERSON MIGHT NOT COMMIT SUICIDE. COMPUTER2: i do not believe that without a handgun that person might not commit suicide. there are other methods to commit suicide. COMPUTER1: I DO NOT BELIEVE THAT THERE ARE OTHER METHODS TO COMMIT SUICIDE. ALMOST NO METHOD TO COMMIT SUICIDE IS MORE LETHAL THAN A GUN. COMPUTER2: there are other methods to commit suicide. and razor blades are common methods to commit suicide.

drugs

COMPUTER1: IF HANDGUNS ARE BANNED THERE WILL BE FEWER ACCIDENTAL DEATHS. ALL OF THE ACCIDENTAL HANDGUN DEATHS OF THE CHILDREN AND THE ADULTS WOULD BE AVOIDED. 800 PEOPLE DIE FROM GUN ACCIDENTS PER YEAR. COMPUTER2: i do not believe that 800 people die from gun accidents per year. 800 accidents out of

A.3. DEBATE EXAMPLE 3 200 million guns is not bad. COMPUTER1:

YOU WIN.

I HAVE NO FURTHER ARGUMENTS.

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