PRAGMATIC CONSIDERATIONS IN MAN-MACHINE DISCOURSE 
Walther v. Hahn 
Research Unit for Information Science and Artificial Intelligence 
UNIVERSITY OF HAMBURG 
D-2000 HAMBURG 13, West-Germany 
Introduction 
This paper presents nothing that has not 
been noted previously by research in 
Artificial Intelligence but seeks to gather 
together various ideas that have arisen in 
the literature. It collects those arguments 
which are in my view crucial for further 
progress and is intended only as a reminder 
of insights which might have been forgotten 
for some time. 
Research on discourse has achieved 
remarkable results in the past decade. The 
standard has been raised from simple 
question answering to dialogue facilities; a 
fact which, as we all know, implies much 
more than only extending the borderline of 
syntactic analysis tn the \]evp\] oF morn than 
one sentence and more than one speaker. 
However, at the same time we all know that 
the reality of discourse is nearly as far 
away as before from what we are able to 
model now. It's certainly not worth 
enumerating all the deficiencies of current 
models and to list what the real goals are. 
It's a matter of every-day experience to see 
the "blatant mismatch between superficial 
human ease and theoretical mechanical 
intractability" (Berwick (2)27). 
The situation seems similar to that of 
modern linguistics, "which has tried its 
best to avoid becoming entangled in the 
complexity of conversation, but has been 
gradually forced in this direction by 
uncooperative data" (Power / dal Martello 
(23). 
Though it is one of the so called "good old" 
traditions of science to eliminate a lot of 
the most difficult questions by saying 'this 
is not our job', in AI, however, from the 
cognitive point of view we must realize that 
everything is our job. "Its dirty work, but 
somebody's got to do it" (Israel (18)). 
This seems rather contrary to what Berwick 
(2) shows in his 'Cook's tour' around the 
geography of dialogue, where everything fits 
together in an overall map and where 
modularity is a virtue: Our knowledge of 
natural discourse processes is highly 
insular without bridges in between; and: the 
reality of discourse is complex in that 
everything is contingent with everything 
else; in fact, nothing is 'modular' in this 
sense (see Fodor (ii)). 
What I will do in this paper is to show all 
this as the patchwork it is and to 
encourage to approximate the alternatives 
seen so far. In a lot of fields of dialogue 
research the discussion often is 
characterized by an 'either-or'-view whereas 
we should try to find a 'as-well-as'- 
solution or even another new path of 
research. 
Looking at the results of our work we have 
to accept at least three lines of progress 
which all have their own merits, namely 
(1) @evelo~ing new concepts, based on new 
integrating ideas, even if only limited 
implementation or other proofs of 
feasibility might be possible at time, 
(2) the unfolding of these ideas by 
theoretical background work and experiment 
in all detail. The result of this work could 
show the intractability of such approaches 
or prove that this approach can be mapped 
onto a known solution (as e.g. Johnson-Laird 
(19) has tried to show for meaning 
postulates and decompositional semantics). 
(3) the exploitation of the ideas in 
constructing working systems which may show 
whether or not the idea passes the 
feasibility test. 
The general feeling in Artificial 
Intelligence now seems to be either 
resignation or particularization of the 
problem of discourse. The alternative after 
all is not doing everything at the same time 
in one single ultimate system or doing 
nothing, but we must go on to fit together 
the great puzzle even if there are a lot of 
missing pieces in areas which we have 
already attacked. 
The first Challenge: 
Perception and Function 
Inteqration of 
The practical view tends to be restrictive 
in its approachto perception because this is 
the world of the naive user of practical 
systems. People involved in natural dialogue 
520 
obviously hear or read only words, move 
objects or manipulate symbols. They know 
that thei\[ intuitions about the role and 
function of the symbols might be wrong, but 
all cognitive actions are triggered by more 
or less physical objects. And, what is even 
more important, naive users are sure that 
the visible words or even cursor positions 
coincide with tile function intended by them. 
It is always difficult, e.g. to demonstrate 
users ambiguities in their utterances. 
People are surprised when you explain 
indirect speech acts to them. 
Scientists, on the other hand, have to 
reconstruct a series of hierarchJ ca\] 
abstract levels and internal representations 
and often enough they get lost in their own 
symbolic maze and have to invent more and 
more artificial tricks to climb out of their 
constructions and still meet the surface of 
the utterance. 
Of course, it is hopeless "to seek meaning 
in the physical properties of utterances and 
formal properties of language. However, the 
simple :\[act is, that speech is merely noise 
until its potential meaning is appeciated by 
the cognitive activity of a hearer" 
(Harris/Begg/Upfold (\]6)) . 
There is a good example which shows that 
there are even cases in which you cannot 
decide whether you are talking about objects 
or words or abstract constructions. Sidner 
(24) introduced the notion of "~egni tlve 
cospecification" for the following example 
to show that some anaphors cannot be 
replaced by a literal antecedent in any 
previous sentence: 
"My neighbor has a monster Harley 1200 
They are real ly huge but gas effJ cient 
bikes" 
Another good example for the non-uniqueness 
of vfsual perception: Conc\]in and Mc\])ona\]d 
( 7 ) tried to built their techniques of 
generating image descriptions on their 
observations what peop\]e found worth 
describing in photographs. But what these 
ff nterviewee found salient was highly 
dependent of the context of the request for 
description. And this is a matter of 
pragmatics o "There is no salience in a 
vacuum" (7) . 
This discrepancy is ref\] coted also in 
Butterworth's (5) 5. and 6. maxims for tile 
I\]nguistic study of conversation which 
state: 
"5. Let the theory do the work! 
6. Let the phenomena guide the theory!" 
In Artificial Intelligence it Js not only 
the practical point that normally 
interaction is restricted to tile screen, the 
keyboard and the mouse, that is, the surface 
of systems is the only visible link for the 
user as well as in principle for the 
knowledge engineer. Moreover, it is 
neccessary to compare always the behaviour 
of a system whith what a user expects to 
see as an indication of the expected 
function, because it guides the intuition of 
the system' s partner anyway. Careful 
concentration on what the user sees and 
expects to see even if the system fails to 
react properly is one of the best means of a 
pragmatically adequate treatment of 
discourse in Artificial Intelligence. 
Some of the addressed problems can be 
reformulated on another level, as 
The 2nd Challenc~-- \]nt~ration of Intuition 
and Idealization 
The representation of knowledge, especially 
the way logicians look at it, has often been 
the starting point of a long discussion, how 
natural, how plausible a specific 
representation is in comparison to 
underlying cognitive processes. Of course 
you can and should (at least to keep 
consistency) map a\] l systematic 
representations onto a logical notation. But 
logicians and linguists all rely on their 
intuition in creating their significant 
examples and counterexamples for 
representation problems. Power and Martello 
( 23 ) critizising ethnomethodology say in 
their maxim (2) : "There is no reason why 
intuitions about invented examples should be 
ruled out as a method of investigat J ng 
conversation" 
Why do they argue with intuition J n respect 
to what they represent but not in respect to 
how they represent it? 
\]it :i s an accepted :i deal. J zation among 
linguists, logic\] ans and Artificial 
Intelligence researchers that input 
sentences must first be represented in an 
internal language. And than we are at home 
in our theories and can start our tricky 
algorithms, in syntact.ic analysis, e.g. we 
norally attach the syntactic categories to 
the input words by means of a "syntactic 
lexicon". _It must always be clear that all 
this is highly counterintuitive for any 
naive speaker. We have in fact no ultimate 
reason for doing so except the argument that 
we see at the moment no way to proceed the 
input in another rule-guided way. 
Normally we have no cognitive reasons for 
choosing exactly the representations we use. 
Consider the J deas of Langacker ' s ( 21 ) 
"cognit:ive grammer". IiJs ideas, though he 
might be fallacious about drawing graphics 
being better than writing down predicates or 
operators, show that there are lots of 
plausible ways to talk about semantics and 
grammar. Doubt\]ess we are often bound to 
topographical or space-oriented concepts in 
our linguistic intuition. 
\[t goes without saying that we understand 
texts and sentences as the primary units, 
not words er morphemes or quantifJcations. 
Our understanding is supported by our visual 
memory, by acoustic memories, and by other 
521 
complex experience in the past. Only when we 
are forced to understand tricky linguistic 
or logical examples or must understand 
defective, illformed or mistyped utterances, 
(and we have no opportunity to initiate a 
clarification dialogue!), only then we will 
start up our analytic linguistic processor 
and check rules, endings, positions of words 
etc. (cf. experiments with garden-path- 
sentences). 
There is no point in arguing with the 
incremental understanding of sentences by a 
listener as he hears each word. We certainly 
do not perform structural analysis word by 
word. At best we check structural 
constraints for our semantic/pragmatic 
hypotheses. 
I would even go further. Presumably the 
intuitions do not contain any clear concept 
of understanding as long as there are no 
misunderstandings (I will come to this point 
later). And even then, as Goodman (12) 
shows, "people must and do resolve lots of 
(potential) miscommunication in everyday 
conversation. Much of it is resolved 
subconsciously with the listener unaware 
that anything is wrong". 
Ellman (8) even claims that "the 
classification of indirect speech acts is 
primarily for analytic purposes and it is 
not stated anywhere that this classification 
is essential to the understanding process" 
To oversimplify: The basic intuition of a 
naive user refers to a SELF and a SYSTEM, 
which works by telepathy, superficially 
guided by the linguistic utterance of the 
user. 
What you will object to is quite clear: 
"Intuition", as used here is a pre- 
scientific label for all the unsolved 
problems of complexity arising in every 
advanced implementation. 
In a sense you are right, because a lot of 
the unsolved problems might perhaps arise 
from the fact that the solutions known so 
far are counterintuitive. But to be serious, 
the notion of intuition alone is too vague. 
We must at least define: the intuition of 
whom? Grosz (13) showed that any application 
oriented natural language interface must 
regard the intuitions (diverging on 
different levels) of a potential user as 
well as of the database expert (knowledge 
engineer). 
Much more general is the objection that 
intuitions concerning plausibility of a 
system's surface exposed to the user is not 
a static affair. In the course of the work 
with a specific system a user will change 
his intuitions about the appropriateness of 
its behaviour and its interpretation of the 
user's utterances. As far as I know there is 
no comparative research on the dynamic 
pragmatics of long-term use of a system. 
A weighty reproach, however, comes from a 
methodological point of view, expressed by 
Caroll and Bever (6). In experiments of 
semantic adequacy ratings one group of test 
persons were heavily biased in their 
intuition by the fact of sitting in front of 
a mirror. This mere matter of the setting 
changed the ratings so much that the result 
of one group would fit well to hypotheses of 
general semantics whereas the other group's 
result would rather back generative syntax. 
Such are intuitions. 
But in any case listening to what people 
think they are doing and the system is doing 
is one of the most surprising heuristics and 
we definitely always need this corrective 
instance to construct systems which are 
pragmaticly more adequate. 
Let us now have a closer look to the 
process of scientific idealization: we 
normally do not only start with the 
translation of the data in a form which we 
can handle, but we also divide the whole 
problem of human discouse into subproblems 
and sub-subproblems. This is, similar to the 
translation paradigm, another "good old" 
tradition which we tacitly accepted; of 
course, we cannot do everything at the same 
time. But this technical routine has been 
internalized in an extremely strong way and 
is not longer only a crutch of science. What 
I adress here is the opposition of 
particularism and holism. 
Israel (18) criticizes the ideal of 
modularity as a concept beeing imported from 
traditional linguistics and psychology. 
Their conceptions of correctness are 
modular, perhaps because of the lack of 
procedural theories and the lower degree of 
formal complexity in their models, because 
of the lack of procedural representations of 
their models by means of implementations. In 
Israel's view the main fallacy in discourse 
models is that "modularists" try to solve 
syntactic and semantic processing first and 
than see what they can do for pragmatics 
additionally. Even syntax in the theories of 
these hopeless "syntacto-semantic 
imperialists" (18) is clearly devided into 
sentence-by-sentence and level-by-level 
processes. And once we have cut the problem 
into pieces we forget even to try to fit it 
together again afterwards. 
In my opinion we are moreover too 
accustomed to boxes and arcs for 
illustrating of our ideas in AI. Figures as 
the following corrupt clear communication: 
I sPoakerl----IE \]----I 'hearor 
t t I. po  o  ,oo I 
522 
In opposition to these simplistic views of 
language, neurolinguistics has shown that 
understanding is a sort of pattern 
(re)construction working freely through 
different levels of abstraction between the 
level of physical perception and 
understanding or the reaction respectively. 
We can apply holistic ideas anywhere: 
Appelt's (i) arguing for unification in 
grammars as a very elegant way to pass 
pragmatic features through different levels 
of a language processing system is a good 
example. 
Another example might be the opportunistic 
planning by Hayes-Roth and Hayes-Roth (17) 
which, from a cognitive point of view, can 
model human planning behaviour in a very 
convincing way. The fact that they start 
with isolated tasks and then put together 
chunks of pre-planned actions is no 
argument for modularity because there is no 
intermediate built-in level of completed 
substructures. So the incremental strategy 
of the HEARSAY II-arehitecture fits much 
better to the holism of the understanding 
process. 
Though there might be other good reasons for 
preferring modular implementations in 
today's work: Let us try to achieve again a 
holistic and intraintuitive model of human 
dialogue processes. 
The 3rd Challen eg~ Inteqration of Different 
Sources of Plausibilit\[ 
The main process of idealizing the data is 
to evaluate the phenomena in respect to 
their importance for further treatment. But 
where do the criteria of this evaluation 
process come from? One possibility is to 
rely on the background sciences e.g. 
linguistics, psychology, sociology etc. 
In comparison to the 70's there is indeed 
much more cooperation with what I called the 
background\[ sciences. As Brady (3) remarkes, 
Artificial Intelligence has overcome the 
first years in which we thought that the 
very specific view and the methodological 
implications of Artificial Intelligence were 
so extremely different from everything in 
the past, that we had better start again 
thinking about language and cognition in our 
own paradigm. 
This has become better now even though I 
think that there is too little cooperation 
with sociology e.g. in questions of partner 
modelling, or multi-user effects. 
There is also a growing interest in AI from 
the other sciences in AI. Walton (27) 
explicitely states, that there is a new 
interest of logicians in a logical theory of 
discourse because of the representational 
work done in AI. There is hope that this 
contact will influence the disadvantageous 
tradition of logics to eliminate everything 
which is not regular enough as some sort of 
pragmatic pollution. 
Cognitive psychology, after decades in the 
declarative and microexperimental paradigm 
(at least in Europe), is trying again to 
sketch more general and broader cognitive 
models. 
However, there are fields in which discourse 
analysis cannot rely on linguistics because 
of the missing explicitness concerning 
procedural aspects of language (see the 
Dresher/Hornstein controversy in Cognition 
4,1976 ff). E.g. modern linguistics is just 
starting to discover language generation. 
But we need even sketchy procedural models 
of understanding, of generation, of 
anaphora, or of spatial perception and 
description today. 
And there is the same holistic reason why we 
cannot simply take the results of 
linguistics or psychology and program them: 
linguists are not used to constructing 
integral models. In their paper-and-pencil 
work there is no need for explicitely 
relating e.g. the view of page 20 to that of 
page 200. Implementation of discourse 
understanding processes ,on the other hand, 
produces systems in which everything must 
fit together. 
A third argument, however, hits linguistics 
as well as AI: We have no well-developed 
linguistics of natural language man-machine- 
communication. This means: no theories about 
language acquisition, generation, 
understanding, partner model, pragmatics, 
etc. of man-machine-communication. 
Evidence from mock-up systems, simulated by 
persons, is methodologically vague and 
mostly too isolated from real application. 
Besides this it is restricted to short-term 
results. Nobody will play the mock turtle 
for months with hundreds of test persons. 
Of course, linguists concerned with man-man- 
interaction have another interest in 
cognition. They do not implement their 
theories, or they do so for methodological 
reasons and not for the construction of 
working integrated software-systems. This 
has another result, namely that empirical 
work in linguistics is concentrated more on 
very genera\] types of discouse (informal 
dialogues, party small-talk etc.) and not so 
much on dialogues in the fields of 
application in which practical AI needs 
natural dialogue examples. 
Kittredge and Lehrberger (20) brought 
together linguists and AI people under the 
notion of "sublanguage". This volume could 
have referred, however, to all the research 
on "technical language" or "registers" done 
in Europe since the early Prague School. 
Meanwhile there are available a lot of 
detailed studies, some highly developed, 
though largely informal theories and a lot 
of statistic material\[ about communication in 
non-social contexts and among experts (for a 
survey see v. Hahn (15). 
523 
This research investigates what in AI is 
sometimes neglected: The semantic and 
syntactic restrictions in technical 
languages, the differences between written 
and spoken language or the effects of 
communication with non-individual 
addressees. 
Wynn's PhD thesis (28) seems to be one of 
the few empirical studies for the american 
office setting. 
Empirical work in this field is necessary 
for plausible performance of application 
oriented systems. McKeown et al. (22), 
although she did not invent the linguistic 
characteristics of their system but based it 
on transcripts of actual student advising 
sessions, admits, that "it would be 
desirable to have much larger set of plans, 
knowledge about their base rates and 
importance, and additional criteria for 
tracking their relevance and likelihood 
during the interaction". 
In the long run we need such research for 
practical systems even in the starting phase 
of designing a system. We will be forced to 
start work with very clear functional 
specifications and will apply much more of 
the techniques of software engineering. 
Let me close this paragraph with a more 
heuristic remark. Some remarkable progress 
in procedural modelling of human language 
abilities has been achieved by looking at 
the problems from the opposite side. 
I will give some examples of this figure- 
ground heuristics: 
Falzon et al. (9), investigating the 
conditions of "natural" technical 
communication, did not look at the 
understanding process of a hearer but at the 
techniques of communicative experts, how 
they guide the the partner in restricting 
his or her linguistic activities. 
Wachtel (26) recommends looking at ellipsis 
as the unmarked linguistic form whereas 
explicit full sentences are to be motivated 
by a specific context. 
Webber and Mays (25) as well as Goodman (12) 
started to do research on misunderstandings 
and misconceptions to get an idea of proper 
understanding; instead of the flow of 
continuous coherent interchanges Hayes-Roth 
and Hayes-Roth (17) Grosz and sidner (14) 
scrutinized interruptions as "a salient 
feature of cognitive processing in general" 
(17). 
Harris/Beg/Upfold regard semantic 
understanding not as a reconstruction 
process: "the hearer does not construct a 
message from components extracted from 
speech but rather narrows down and refines a 
message by successively rejecting an 
inappropriate information from a general 
message" (16). 
524 
By the way, this heuristics holds even for 
the style of publications: It is a good 
tradition esp. in American reports to 
discuss the limitations and the shortcomings 
of one's own approach, which is not often 
heeded in European papers. 
The 4th Challen eg~ In q~ration of Pindinq 
Procedures, Representations, a nnd Evaluation 
Processes 
In this last paragraph I will follow another 
line of the holism argument: In contrast to 
linguistics, in AI every process must be 
defined on at least three levels. 
I) how to find in the data those 
features addressed by the theory, 
2) how to represent them 
3) how to infer on them or to 
evaluate the representation 
In the intuition of the speaker/hearer this 
is in fact one simple process. Meta- 
utterances of speakers never will refer to 
only one of these processes. 
Too much work in discourse analysis lacks 
one of these three levels. Of course, 
specific work may concentrate on one aspect 
without elaborating the others. But the 
arguments for the approach must come from 
all three processes. 
Some examples: 
You can represent the process of running a 
car ( a similar example was first indroduced 
by Faught (i0) as a sequence of choices, 
because one can observe all these actions 
and objects: 
- foot: left / right 
- hand: left / right 
- movement: put on / release / 
move 
- device: clutch / accelerator / 
gearshift / brake 
But in real driving actions you will never 
find a moment, when a driver has to choose 
between, say, the brake and the clutch 
directly. There are patternd sequences 
representing the plans of "go faster" or "go 
slower" etc. in which the elementary actions 
occur on different places, but everything 
seems to be compiled in some way. 
Theoretical work often starts with 
statements like "Let (x(y)z(a)) be the 
representation of of the sentence (7c)" It 
is nowhere explained by which detection 
procedures this representation can be 
obtained or whether there is even the 
slightest chance of defining an analysis 
algorithm which maps (7b) onto (x(y)z(a)).Is 
cognitively plausible reasoning possible on 
this structure? 
Empirical work often starts with statements 
like "The speaker is here slightly 
influenced by the fact that ..." Does that 
mean to introduce some sort of predicate 
SLIGHTLY INFLUENCED (x,y)? How can this 
specification be found in the linguistic 
data and how can you infer on that 
(Following Butterworths (5) 4th maxim 
"Remember that conversationalists talk".) 
The tight connection of analysis, 
representation and eva\]uation is necessary, 
among others, because every explanation of 
the system must be based on some sort of 
self-inspection of the system. But a system 
cannot answer to a request for 
clarification: "I could find a discourse 
constituent unit but i was not able to 
construct a discourse unit out of it". 
It is not reasonable to address features of 
data which cannot be represented in a 
tractable way and cannot be evaluated for 
plausible processes on higher levels. Or to 
invent representations for which you cannot 
find a mapping from the data. 
what is the use of an inference mechanisme 
for an natural language interface, if it 
cannot handle vague natural language 
quantifiers detected by the parser? 
We criticize all these partial views to 
discouse understanding processes a\]so for 
another reason: 
We must show the plausibility of the 
detection procedures, the representation and 
the inferences also under the natural 
conditions of mass data, that: means 
e.g.multiple views on a subject, or 
remembering and forgetting. Most of the 
proposals for dia\]ogue structures never have 
occupied with the mass phenomena. What will 
happen, when all the heterogeneous details 
are represented, when you will have several 
thousand non-uniform inference rules? 
Of course we ever will discuss thoroughly 
the very features of natural dialogues which 
we cannot handle today, and start with 
fragments. But to propose e.g. any arbitrary 
representation without connection forwards 
and backwards is only a tiny step towards 
the solution of the discourse problems. Our 
knowledge of discourse processes is at \].east 
so that we cannot any longer design isolated 
structural fragments of the analysis and 
generation process. 
Let me summarize: Cognitively sound 
approaches to discourse processes must start 
once again to take seriously the user and 
his intuitions about man-machine- 
interaction. We must free our general 
concepts from the shortcomings of 
modularity, that means to accept the equal 
importance of discovery procedures, 
representations, and evaluation. The 
reliability of one of these processes can 
only be justified by arguments of both 
others. We should exploit the results of the 
background sciences linguistics, psychology 
and social science as far as they support a 
pragmatic and procedural view of discourse. 
All this to set out a new pragmatic and 
holistic view of our natural, flexible, 
ef\[icient and " whatsoever " way of 
communication. 
Acknow i edgementzs_ 
I am grateful to Tom Wachte\] for essential 
discussions and for revising the English 
version of this paper. 
An invitation to the 'maison des sciences de 
l'homme' at Paris gave me the time to write 
the paper. 
The preparation of the paper was supported 
by the ESPRIT project LOKI. 

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