A Computational Model for Arguments Understanding 
St~phane Guez (*) 
Department of Computer Science 
University of Rochester 
Rochester, NY 14627 (USA) 
1. Introduction 
This paper presents a computational model for the 
understanding of arguments in natural language 
dialogues. Previous work on argumentation in 
Artificial Intelligence has been mainly focused on the 
conceptual aspects. But argumentation is also a 
linguistic phenomenon. Language provides 
structures to express arguments, to orient the 
argumentative interpretation of utterances, or to 
present a new concept or a new piece of information 
as an argument in favor of or against a certain 
conclusion, independently of the actual contents of 
these propositions. Argumentation also affects the 
discourse structure. The order in which arguments 
can be uttered is constrained. 
The purpose of this paper is to describe the different 
aspects of argumentation, and to propose a model 
which integrates the different levels of analysis of 
argumentative phenomena: the conceptual level, the 
linguistic level, and the discourse level. 
2. What is Argumentation? 
The study of argumentation involves the 
understanding of the propositional content of 
utterances, as well as the analysis of their linguistic 
structure, the relations with the preceding and 
following utterances, the recognition of the underlying 
conceptual beliefs, and general understanding within 
the global coherence of the discourse. Argumentative 
analysis relies on several sources of knowledge: 
linguistic constraints, domain dependent conceptual 
relations, and discourse structure. None of them is 
sufficient by itself for a complete analysis, but they all 
contribute to it, especially if one source of information 
is incomplete, for instance if the beliefs of the 
speaker are unknown or unusual or if the semantic 
content is ambiguous. In any dialogue, one of these 
sources may be missing, without preventing the 
speakers from fully understanding the statements 
and positions of the other participants in the 
conversation, providing that the speakers remain 
coherent in the way they organize and express their 
beliefs. 
It is not possible to view argument understanding as 
a linear process, going from syntactic and semantic 
analysis to conceptual interpretation and global 
pragmatic understanding. In many cases, the very 
purpose of a conversation is to allow the participants 
(*) Current address of the author: BULL - CEDIAG, 68 
Route de Versailles, 78430 Louveciennes (France). 
E-mail: guez@cediag.bull.fr 
132 
to present their views on a subject. Therefore, the 
hypothesis that the other participants can rely on a 
complete description of the beliefs of the speakers to 
fully understand their arguments does not hold in real 
situations. But the linguistic structure of the 
arguments may often be analyzed independently of 
their content, and reveal constraints from which we 
can derive the information that can make up for these 
gaps in the knowledge about the domain and the 
speakers beliefs and intentions. A complete rnodel of 
argument understanding must also include a model 
of learning. It is our intention to focus on non 
conceptual sources of knowledge, main\[y the 
structural constraints which can provide essential 
information to understand arguments. 
An argumentative analysis is aimed at understanding 
how the arguments relate to each other: what is said, 
in favor of which proposition, based on which beliefs 
and towards which intention. 
Consider the following example, adapted from 
\[Cohen 1987\]: 
Jones has lots of experience. (i) 
He has been on the board i0 years. (2) 
And he's refused bribes. (3) 
So he's honest. (4) 
He would really make a good president. (5) 
To understand this discourse is to figure out how 
propositions relate to and support each other: 
(2) ---> (1) ---\ 
.... > (5) 
(3) ---> (4) ---/ 
Prior conceptual knowledge about the domain, as 
well as the discourse properties of so and and helps 
to guess at once the structure of the arguments in 
the sequence of statements in this example. A closer 
analysis may also reveal that we do not really need a 
complete prior knowledge of all the conceptual 
relations involved in the example. The use of so in 
(4) not only informs us that the following proposition 
is given as conclusion of the previous one (3), and 
not the other way around, but also tells us that this is 
the only reasonable possibility. Any proposition in (4) 
had to be a valid conclusion for (3) and we could 
have learned from the entire sequence that the 
speaker views refusing bribes as a definite reason to 
declare someone honest. 
Consider now the following dialogue between two 
speakers planning a big family reunion: 
A: How about asking your sister to come 
too? (al) 
B: The kids will love to see their 
cousins. (bl) 
B~d; it's such a long trip. (b2) 
Besides you know how I feel about my 
l>rothez~in-law. (b3) 
(b3) contains a strong semantic ambiguity, though 
the reader will not have any doubt about its overall 
meaning: it is a rejection of (all. But how I feel may 
mean I don't like him as well as I like hhrJ. The 
interpretation must rely on contextual information and 
structural properties. A careful argumentative 
analysis reveals that the structure of the dialogue (x 
but Y besides Z) strongly constrains the 
interpretation of (b3) almost independently of the 
actual content of (b3). Let us consider two variants of 
B's last reply: 
B: Besides, I will enjoy seeing my 
nephews. (b4) 
B: Besides, you know how I love my 
nephews. (bS) 
(b4) sounds like an incoherent statement, while (bh) 
could only be interpreted as sarcasm (how I love 
would just mean Ihate). The linguistic structure is so 
strong that, whatever follows the final besides, it can 
only be interpreted as an argument to reject (all and 
we do not need any prior knowledge about B's 
beliefs to understand this dialogue. 
3. Knowledge Sources for the Analysis of 
Arguments 
3.1. The Conceptual Analysis of Arguments 
Artificial Intelligence work on argumentation has 
been essentially focused on the conceptual level, 
mainly because the argumentative analysis of natural 
language dialogues has been generally considered 
as a conceptual only problem. So previous work 
emphasizes problems related to the logical structure 
of arguments and the representation of domain 
knowledge. Arguments are propositions supporting 
other propositions, and the analysis of a discourse 
results in a tree showing how propositions are 
expressecI in favor of or against each other, relative 
to a knowledge base of basic relations and 
arguments. 
\[Flowers 1982\] proposes to represent the history of a 
dialogue between two opponents in an argument 
graph and studies the strategy to generate the best 
next turn. The analysis is strictly conceptual and 
linguistic issues are mostly ignored. 
Robin Cohen proposes a model for the 
understanding of arguments in discourse \[Cohen 
1984, Cohen 1987\]. In her perspective, all the 
relations between arguments which are understood 
from the given text must be supported by conceptual 
knowledge of general or particular beliefs about what 
is a good argument for what. Given a sequence of 
statements, the question is to figure out how the 
propositions relate to each other. For a sequence of 
two related propositions, the first proposition may 
support the second proposition given as conclusion, 
or the other way around: the conclusion is given first 
and its justification fellows. With more statements, 
the complexity of the computation increases 
dramatically as both schemes may be mixed and as 
more than one proposition may be expressed to support 
a conclusion. She give~ an algorithm to build 
the underlying conceptual structure incrementally 
which takes into account the role of clue words to 
limit the search about where the current sentence 
should be attached in the structure. The whole 
process relies heavily on the information provided by 
an "Evidence Oracle". The oracle contains a list of 
evidence relations, and given two propositions, tells 
whether the first one can be given as an argument 
for the other. 
3.2. Limits of the Conceptual Analysis 
The major difficulty raised by an exclusively 
conceptual treatment of argumentation is the 
problem of incomplete knowledge. If we do not use 
any other source of information or constraints about 
the discourse, only arguments supported by prior 
knowledge recorded in the knowledge base can be 
recognized and properly understood. Thus in such 
systems, the assumption is made that the complete 
set of beliefs of the speaker is available. We consider 
this assumption too strong to be fully acceptable, not 
only because it seems difficult to represent such a 
large and complex amount of knowledge, but more 
fundamentally because in many cases the very 
purpose of argumentative discourse is to present 
new arguments never expressed before, to reveal 
the beliefs and intentions of the speaker, and to 
present, for the first time, certain propositions as 
arguments in favor or against certain conclusions. 
In general, incomplete knowledge about the beliefs of 
the speaker does not prevent the hearer from fully 
understanding all the arguments. In fact, new 
knowledge is learned while the understanding 
process is taking place. On the other hand, 
misunderstanding is a rather common phenomenon 
in human communication, and there is 
misunderstanding as soon as the speaker's 
discourse relies too much on knowledge which is not 
explicitly stated or on implicit relations which are not 
shared by the hearer. It should be also noted that it is 
common for the participants in an argumentative 
dialogue to intentionally use locally ambiguous 
formulations to express their views, while the overall 
orientation of their discourse is perfectly clear to the 
hearer. 
Another major issue is whether or not to consider 
arguments as logical implications. To a certain 
extent, the natural relation "supports" shares some of 
the properties of the logical implication. Of course, 
this relation only makes sense when there is a 
semantic connection (or even a causal relation) 
between the terms, while the truth value of an 
implication is completely independent from the 
semantics of the propositions it connects. If it is 
acceptable to a certain extent to consider 
argumentative relations as logical implications and to 
perform logical inferences on them during the 
understanding process, very often it will be used for 
these inferences uncertain knowledge, default 
general relations, assumed knowledge or even 
relations which have just been learned and do not 
have a very high level of plausibility. It seems at least 
appropriate to be careful about a logical treatment of 
argumentation in a general model of argument 
understanding which intends to take into account 
other aspects of argumentative phenomenon, 
beyond the conceptual aspects. 
133 
3,3. The Linguistics of Argumentation 
The linguistic level has been relatively neglected in 
AI work on argumentation. Flowers, though dealing 
with natural language dialogues, makes almost no 
account of the linguistics of argumentation. If Robin 
Cohen proposes a linguistic analysis of the structure 
of argumentative discourse, she seems more 
concerned with discourse structure than with 
argumentation per se. She studies the role of clue 
words, but essentially their effect on the organization 
of discourse. This study is very interesting since it 
reveals many constraints imposed by the use of clue 
words on the order and structure of arguments. For 
instance, any proposition following phrases like in 
particular or in addition will go in the same direction 
as the previous part of the discourse and provide 
additional arguments in favor of the point defended 
by the speaker. The study also shows very well that 
there are rules about how arguments in favor of the 
point in case or against it can be mixed or organized 
into a coherent discourse. But the step Robin Cohen 
does not make is to truly take into account the 
argumentative value of clue words, in order to avoid 
a systematic use of the Evidence Oracle. Because of 
its perspective, her work sometimes ignores 
properties of clue words which specifically affect 
argumentation. 
Outside of AI research, Oswald Ducrot has 
developed a linguistic theory of argumentation 
\[Anscombre & Ducrot 1983\]. His concern is not to 
study the conceptual structure of arguments raised 
by two opponents in a debate, but how linguistic 
structures affect argumentation. His contribution to 
the study of argumentation is part of a larger 
framework, referred to as "integrated pragmatics", 
whose goat is to demonstrate that linguistic 
structures (syntax) and pragmatics must be taken 
into account together in the process of discourse 
understanding. 
According to Ducrot, language provides specific 
structures to express arguments and constrain the 
discourse. Certain words orient the argumentative 
interpretation and the continuation of the discourse, 
independently of the informative content it may carry. 
Ducrot identifies the linguistic constraints which rule 
the presentation of a proposition P used to make the 
hearer accept a conclusion C. It is not enough that P 
be conceptually a good reason to accept C: the 
linguistic structure of the utterance of P must also 
satisfy certain conditions in order for it to be, in the 
current discourse, an argument for C. For example, 
to say even A is to present A as an argument 
oriented towards some conclusion C and stronger 
than the arguments presented so far. It is 
independent of the content of A and whether A is 
really a good argument to defend the conclusion C. 
Ducrot's work does not specifically concern clue 
words, but any linguistic "operator" which may affect 
argumentation. The argumentative features of 
operators that are described at the linguistic level can 
be viewed as constraints that affect the interpretation 
of utterances containing such linguistic structures. 
For example, to say A but B is to present A as an 
argument in favor of some conclusion C and to 
present B as an argument in favor of the opposite 
conclusion not C. The overall argumentative 
orientation of A but B is not C. This description of the 
use of but is independent of the actual instantiation of 
134 
the argumentative variables A, B and C. This 
description takes into account the pragmatic role of 
but and is more general and precise than the 
traditional description where A and B are just viewed 
as propositions with some kind of opposition. 
A very interesting point raised in Ducrors theory is 
the distinction and the independence between the 
informational level and the argumentative level, as it 
is developed in \[Raccah 1987\]. The argumentative 
use of an utterance depends only partially on the 
informational content of this utterance. In particular, it 
is very often the case that while the utterance of a 
proposition P may provide very good reasons to 
accept a conclusion C, it is impossible to use P in a 
discourse as an argument in favor of C. For instance, 
if the utterance You are nearly on time carries the 
information You are late, it cannot be used as a 
reproach and followed by something like You must 
apologize. The linguistic structure, in this case the 
use of nearly (it would be the same with almost), 
constrains a proposition to produce an argumentative 
effect exactly opposite to what could be expected 
from a strictly logical analysis of the propositional 
contents. Consider now the two propositions This car 
bums little oil and This car burns a little oil: they carry 
exactly the same propositional content, but if we 
agree with the belief that to burn oil is not a good 
thing for a motor, then we can very well say This car 
bums a little oil but the motor is in good shape, while 
This car burns little o# but the motor is in good shape 
sounds inappropriate, which can only be explained 
by the linguistic structure used: a combination of little 
or a little with but. If we assume that speakers are 
perfectly coherent, the latter utterance can even be 
interpreted as the expression of the belief that to 
burn oil is a good thing. Someone who really knows 
nothing about car mechanics would very likely 
interpret things that way. Argumentation is not at all 
exclusively determined by the conceptual relations 
between the content of propositions. 
However, the studies of Ducrot on connectives are 
often long and thorough and reveal subtle aspects 
which go far beyond any possible reasonable 
formalization attempt. Raccah \[Raccah 1987\] made 
several contributions to provide a rigorous and 
formalized account of this work. Though he is 
concerned with applications within an Artificial 
Intelligence framework, his main goal is to define 
theoretical semantics of natural language. He has not 
try to define the role argumentation could play in an 
integrated computational model, and his attempts 
therefore cannot be articulated within a larger theory 
of context or a model of discourse processing. 
3.4. The Structure of Argumentative 
Discourse 
The structure of argumentative discourse is also 
constrained by the rules which apply to any 
discourse, and the same concepts can be used to 
describe it \[Grosz & Sidner 1986\]. The analysis of 
contextual information is essential and notions such 
as the focus have their counterpart in argumentative 
dialogues: keeping track of what is currently the 
object of the debate contributes to the dialogue 
segmentation. The continuity of the point in case 
provides an additional criterion for the definition of a 
segment in argumentative discourse. 
3 
Robin Cohen's work shows how clue words affect the 
order in which arguments are uttered, and she 
identifies rules which constrain the structure of 
argumentative discourse. Argumentation is 
essentially a relational phenomenon: how do the 
propositions which are uttered in a discourse relate 
to each other. Ducrot's work directly addresses this 
point: it is a study of the constraints that rule the 
orientation and continuation of discourse. The 
discourse structure provides a framework in which 
new propositions are attached to when they are 
analyzed. 
3.5. hltegrating the Analysis of Arguments 
Most of the work on argumentation that we have 
presented is usually mainly focused on one aspect of 
argumentation and tends to reduce the whole 
problem to these aspects. From all the examples we 
have previously mentioned, it becomes clear that 
understanding arguments is not only or specifically a 
conceptual problem, nor is it a linguistic problem, but 
it is a combination of conceptual, linguistic and 
discourse issues that must be dealt with 
concurrently. An "argumentative operation" occurs 
when an explicit proposition is presented in favor of 
or against another proposition, which may remain 
implicit. An argumentative operation is characterized 
by the propositional content of the argument, the 
linguistic structure used to express it, and the 
discourse context in which it is uttered. 
We view the argumentative analysis of a dialogue as 
the identification of the argumentative operations in 
this dialogue, along with the explicitation of the 
constraints and relations which support each 
operation, at the linguistic, conceptual and discourse 
levels. Our model then consists of several modules, 
each one providing an analysis which contributes to 
the understanding of argumentative operations. Here 
is a brief description of each level of analysis: 
° At the Linguistic Level, the use of connectives is 
analyzed as constraints put on the interpretation of 
discourse. Knowledge consists mainly of a detailed 
description of the properties of linguistic structures 
which play an argumentative role. 
At the Conceptual Level, the conceptual 
structure of the arguments is analyzed. The 
knowledge base contains common sense relations, 
hierarchies of concepts and argumentative 
relations distributed in different belief spaces. It 
also describes the relative strength of arguments. 
This level has also the ability to make hypotheses 
on new beliefs and check their plausibility. 
- At the Discourse Level, the discourse structure is 
built incrementally and the position of each 
utterance within the structure is recorded. This 
level of analysis keeps track of the argumentative 
focus of the discourse as well as constraints which 
hold through several sentences. 
The essential idea is to integrate the different levels 
of analysis of argumentative discourse in order to 
come out with an interpretation which is coherent 
with all these levels, within each level and between 
them. 'The constraints described at the linguistic level 
define relation of coherence between the linguistic 
structure and the propositional content of utterances. 
We do not consider each participant in the dialogue 
as a perfectly logical actor, but only relations which 
are consistent with the current content of the bases 
can be hypothesized when conceptual knowledge is 
missing. The analysis of the discourse structure is 
used to maintain the global coherence of the 
discourse. In general, we will always consider that 
each participant in a dialogue only utters coherent 
statements. 
A complete theory of argumentation must include a 
theory of learninq. Learning new conceptual 
knowledge, in the course of the argumentative 
analysis, occurs when linguistic and discourse 
constraints can balance the lack of appropriate 
conceptual relations to interpret a particular 
utterance. The identification of new argumentative 
rules may raise conflicts between local and global 
coherence. Consistency with previously existing 
knowledge must be checked before integrating 
learned rules into the base. It is acceptable to 
conclude that a speaker does not follow a common 
belief, but a speaker can not contradict him/herself. 
4. A Computational Model 
Our model consists of several module. Each module 
contributing to the general understanding process by 
providing a specific set of constraints resulting from 
the analysis of the input: 
- the Conceptual Base contains all the domain 
conceptual relations. It is divided in several 
spaces, one for general common knowledge 
shared by all actors, except otherwise specified, 
and one space for each speaker to record his/her 
particular beliefs. 
- the Relation Finder derives appropriate relations 
from the conceptual knowledge represented in 
canonical form. 
the Base of Linguistic Constraints describes 
each argumentative operator. 
- the Context Analyzer keeps track of the local and 
global topic of the conversation, the argumentative 
orientation of the current or previous segment, and 
incrementally builds the discourse structure. 
- the Argumentative Analyzer actually computes 
the argumentative orientation of an utterance or a 
complete turn, taking into account the contextual 
constraints as well as the linguistic constraints.. 
- the Learning Module is activated when there is a 
gap in the available conceptual knowledge, 
resulting in the impossibility to account for the 
coherence of the current turn in the dialogue. This 
modules makes hypotheses for new relations and 
checks their plausibility and consistency with what 
is already known. The Learning Module is able to 
update the belief space of the current speaker. 
4.1. Representation of Conceptual 
Knowledge 
Conceptual knowledge is made essentially of facts 
and rules. Facts concern independent propositions, 
while rules describe argumentative relations between 
propositions. For instance: 
weather (new-york, fine) 
argument (for, wok'ks-hard (X) , 
good-student (X)) 
argnment (against, lazy (X) , 
good-student (X)) 
135 
We use the operator "opposite" to consider the 
opposite of a proposition. This operator is not the 
logical negation, but we have the following rules of 
equivalence: 
arg~ment (against, A, B) i8 equivalent to 
argument (for,A, opposite (B)) 
argument (against, A, B) is equivalent to 
argument (for, opposite (A) , B) 
opposite (opposite (X)) iS equivalent to x 
Knowledge is distributed into different belief spaces. 
By default, general knowledge is shared by the 
actors. Knowledge about semantic hierarchies is 
independent from belief spaces. 
It is very important to insist that argumentation 
relations can not be assimilated to logical operators 
and manipulated as such. The argumentative relation 
"in favor of" is not processed as a logical implication. 
Truth values do not matter very much to interpret 
arguments, since we are mostly interested in the 
relations between propositions. In fact, the truth 
value of individual propositions matters all the less 
that in general, nothing can be logically deduced 
from the combinations of facts and argumentative 
rules. 
An argumentative rule in not a description of the set 
of conditions that must be met for a certain 
conclusion to be true. A rule only defines one 
argument for a conclusion: it usually is a partial 
argument. If this argument holds, there may be at the 
time other arguments which hold and go against the 
same conclusion. This is the very source of any 
serious argumentative debate: opponents will raise 
arguments which are believed, by both, to hold, but 
which go in opposite directions concerning the point 
of the debate. 
For instance, nice weather is surely a good argument 
to go for a walk, though it is not a sufficient condition 
to take such a decision. A lot of work to do is a very 
good argument which goes against the suggestion of 
a walk. Both "nice weather" and "a lot of work" can 
hold together, and there is no way to make any valid 
reasoning to conclude about going or not going for a 
walk. A speaker could express both facts in a 
discourse: what we need to understand his/her point 
is information about which fact is held as an 
argument stronger than the other. 
The need for ways to compare the relative strength 
of arguments illustrates once again the 
inappropriateness of a logical model to handle the 
process of understanding arguments. It is the relative 
strength of propositions towards a certain conclusion 
which determines the outcome of the discourse. The 
predicate stronger asserts the relative strength of 
arguments towards the same conclusion. It takes 
three arguments, the two propositions to be 
compared and the conclusion intended by these two 
propositions. The predicate S£ ronger-opp 
asserts the relative strength of arguments towards 
opposite conclusions. It takes three arguments, the 
two propositions to be compared and the conclusion 
intended by the first one (while the second 
proposition intends the opposite of the given 
conclusion). For instance: 
stronger(need-exerclse, nice-weather, 
go-for-a-walk) 
stronger-opp(lot-of-work, nice-weather, 
136 
opposite(go-for-a-walk)) 
4.2. Representation of Linguistic Knowledge 
Our model uses first order logic to describe relations 
and constraints. We represent the knowledge 
attached to argumentative operators as a list of local 
constraints which are satisfied when the operator is 
used. For but and almost, we have for instance: 
(A but B) 
argument(for,A,C) 
argument(against,B,C) 
stronger-opposite(B,A, opposite(C)) 
argumentative-orientation( 
operator(but,A,B), opposlte(C)) 
(almost A) 
argument(for,A,C) 
argumentative-orientation( 
operator(almost,A), C) 
The predicate argumentative-orientation 
is used to assert the final orientation of an expression 
containing an operator or a connector. The 
orientation is given as a propositional content. The 
constraints which are not explicitly present when the 
expression is uttered are assumed to be asserted at 
the time of the utterance. 
4.3. Representation of Discourse Structure 
The input and output uses the same basic data 
structure, which is a complete description of the 
dialogue. The structure is augmented when 
constraints are taken into account and conclusions 
found. Descriptions use a features list format. 
The dialogue is described as a hierarchy, according 
to the segmentation of the dialogue between turns 
(complete intervention of one speaker) and individual 
utterances. Initially, the structure only contains input 
information about the first utterance. 
The hierarchical structure is then built incrementally. 
Information is added as soon as it is available, as the 
result of the analyses performed on the input. Within 
the discourse structure, at each level, the topic and 
the argumentative orientation are recorded. 
4.4. Algorithm for the Analysis of Arguments 
The analysis of a dialogue is performed as an 
incremental process. The basic algorithm consists of 
the following steps: 
- listing the contextual constraints 
- listing the linguistic constraints resulting from the 
use of clue words 
- searching for argumentative relations coherent 
with the previous constraints 
- computation of the argumentative orientation 
It is extended to include the computation of 
contextual constraints and the derivation and 
learning of new conceptual relations. We keep track 
of a global topic as well as a local topic, often 
identified as the argumentative orientation of the 
current segment. An analysis is first attempted using 
the available concepts, and if it fails, the hypothesis 
mechanism is activated. Hypotheses added to a 
belief space can be later retracted to satisfy global 
coherence. Hypotheses may be made about missing 
conceptual knowledge, even in the case where these 
new relations are incompatible with default common 
knowledge, as long as this process results in a global 
interpretation which accounts for the coherence of 
the current utterances. The plausibility and 
consistency of new hypotheses are checked by 
looking for possible contradictions with existing 
knowledge, interpreting for this task argumentative 
relations as logical implications. 
5. Implementation and Example 
We have realized an implementation in Prolog. It is 
able to analyze a dialogue, to compute 
argumentative orientations and to learn new 
conceptual relations when necessary. The 
syntactic/sernantic analysis is not implemented. 
Dialogues are represented with features structures. 
The discourse constraints are represented as a set of 
rules in Prolog describing the process of any new 
utterance. For each operator, a set el linguistic 
constraints is listed. It is a list of conditions to be 
satisfied to complete the processing of the current 
utterance. 
Here is a trace of the automatic processing of the 
second example given in the introduction: 
U1 string: "Mow about asking your sister 
to co,r4~ too? (al) " 
content: ask sister to come(b) 
mode: suggestion 
orientation: ask sister to come(b) 
U2 string: "The kids will love to see 
their cousins. (bl)" 
content: kids love to see cousins 
mode: affirmation 
argument : 
arg~n~ent (for, kids love to see cousins, 
ask sister to come(b)) 
orientation: ask sister to come(b) 
U3 striLng: "But it is such a long trip. 
( b2 ) " 
operator: but 
content: long trip 
mode: affirmation 
argument s : 
ai:fp/ment (against, 1 ong t rip, 
ask sister to come(b)) 
orientation: 
opposite (ask slster to come(b)) 
U4 string: "Besides you know how I feel 
about my brother-in-law. (b3) " 
operator: besides 
content : 
how feel about brother in law(b,bad 
%% at the beginning~the second argument is 
%% non hlstanZlated 
mode: affirmation 
argument s : 
argt~ment ( for, 
how feel about brother in law(b,bad) 
opposite (ask sister to come(b))) 
orientation: 
opposite (ask sister to come(b)) 
%% the final orientation of B% turn is given 
%% by the orientation of the last uttersnce 
If we replace (b3) by (b3'): 
B: Besides yo~1 know :I: fee.\], good abo~t ~Ly 
brother-in--\] aw. (b3 ' ) 
the system will finds a coherent interpretation, by 
making the following hypothese: 
Asserting the NEW ARGI/MENT: 
hyp argt~ent (against, 
hot~ feel about brother in law (b, good) , 
ask sister to come(b)) 
6. Conclusion 
We have proposed a computational model which 
provides a more complete account of argumentation 
in discourse than what has been proposed before. 
Major directions for future work concern 
irnprovements in the description of linguistic 
operators, and the integration within a larger model 
of discourse processing which would include speech 
acts analysis and plan recognition \[Allen & Perrault 
1980\]. Within this context, we believe our work to be 
a useful contribution to the automatic processing ~,f 
natural language dialogues. 
137 
6 
Modelling with 

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