A Computational Model of Social Perlocutions 
David Pautler and Alex Quilici 
University of Hawaii at Manoa 
Department of Electrical Engineering 
2540 Dole St. Holmes 483 
Honolulu, HI 96822 
Abstract 
The view that communication is a form of action 
serving a variety of specific functions has had a 
tremendous impact on the philosophy of language 
and on computational linguistics. Yet, this mode 
of analysis has been applied to only a narrow range 
of exchanges (e.g. those whose primary purpose is 
transferring information or coordinating tasks) while 
exchanges meant to manage interpersonal relation- 
ships, maintain ufacen, or simply to convey thanks, 
sympathy, and so on have been largely ignored. We 
present a model of such Usocial perlocutions" that 
integrates previous work in natural language gener- 
ation, social psychology, and communication studies. 
This model has been implemented in a system that 
generates socially appropriate e-medl in response to 
user-specified communicative goals. 
1 Introduction 
The importance of viewing utterances as not simply 
statements of fact but also as real actions (speech 
acts) with consequences has long been well under- 
stood (Searle, 1969; Austin 1975; Grice 1975). As a 
result, it is important to study not just the formal 
aspects of language forms but also how speakers use 
different forms to serve different functions. For ex- 
ample, one function of the act of informing another 
person is to make the person aware of a state of af- 
fairs; similarly, one function of promising is to secure 
the return of a favor. 
Unfortunately, the study of speech acts has been 
largely limited to the collection and clmmification of 
act types and the conditions for appropriate use of 
each type (Searle 1969; Wierzbicks 1987). The range 
of functions, or perlocu~iot;~ry effeef~, served by dif- 
ferent act types has been largely ignored. In partic- 
ular, there has been little or no work on the impact 
that speech acts can have on social attitudes and 
behavior. Yet, without an account of how commu- 
Professor WHITNEY, 
Thank you for your invitation. \] 
Unfortunately, I will not be able to give 
a talk at THE U OF M COMPUTER 
SCIENCE DEPARTMENT on APRIL 14, 1998. 
I regret that I must decline. \] 
I 
I have a previous commitment. \[ 
You may want to invite DAN VLASIK in my place. 
! He is well-acquainted with the work we do here \] 
I at McCORMICK SYSTEMS. 
If you would like to pursue this option, please 
contact him directly at (808) 555-1973. 
Figure 1: A LetterGen Output Sample 
nlcation can affect social situations, it is impossible 
to construct systems that are capable of generating 
socially appropriate text. 
This paper provides a computational model of 
aocial perlocutionJ, and it describes how this model 
has been used to construct an automated system, 
£etterGen~ for generating socially appropriate e-mall 
messages and letters. This system takes general 
communicative and social goals from the user, such 
as demanding action or expressing congratulations, 
queries the user about subgoals and pertinent back- 
ground information, and generates the text of an 
appropriate message by planning individual speech 
acts. 
As an example, Figure 1 shows a message gener- 
ated by LetterGen in response to an input goal to 
decline an invitation politely. In this example, the 
writer was invited by the addressee to travel and give 
a talk, but the writer had a previous commitment 
and must decline. However, the writer knows some- 
1020 
one who could give the talk in his place. The system 
planned s set of speech acts and realized each as a 
clause or phrase using a text template library. These 
acts include (1) thanking, (2) declining-request, (3) 
apologizing, (4) making-excuse, (5) advising, (6) as- 
suring, and (7) requesting. 
Most of the text in the letter is devoted to ad- 
dressing the writer's social goals of being polite and 
helpful. In contrast, a letter writer concerned only 
with informing the addresee that he was not partic- 
ipating would likely say little other than "I won't be 
giving a talk at your event n, a socially inappropriate 
response. 
2 Previous Research 
Our work builds on results from three disparate ar- 
eas: natural language generation (NLG), communi- 
cation studies, and social psychology. 
The NLG community has focused on a small sub- 
set of the five generally accepted categories of speech 
acts (Levinson, 1983): 
1. Representatives--statements given as true de- 
pictions of the world (e.g., asserting, conclud- 
ing). 
2. Directives---statements attempting to per- 
suede the hearer to do something (e.g., order- 
ing, advising, warning). 
3. Commissives----statements that commit the 
speaker to a course of action (e.g., promising, 
accepting a request, taking a side). 
4. Expressives---statements expressing a psycho- 
logical state (e.g., apologizing, congratulating, 
condoling). 
5. Declarations---statements effecting an immedi- 
ate change in the institutional state of affairs 
(e.g., christening, firing from employment). 
In particular, research in NLG has been limited 
to one type of representative (i.e., informing) and 
one type of directive (i.e., requesting), and it has 
further focused on informing's potential to con~/nee 
the hearer of some fact and requesting's potential to 
persuade the hearer to do some action (Allen et al., 
1994; Appelt, 1985; Bruce, 1975; Cohen and Per- 
fault, 1979; Hovy, 1988; Perrault and Allen, 1979). 
As a result, it has largely ignored speech acts in other 
categories, such as promising, advising, and credit- 
ing, as well as their potential perlocutionary effects 
of creating airnnity between speaker and hearer, se- 
curing future favors for the speaker, and so on. 
In contrast, research in communication stud- 
ies has explored strategies for persuading, creating 
affinity, comforting, and many other interpersonal 
goak (Daly and Wiemann, 1994; Marcu, 1997). For 
example, the strategies for persuading include not 
only requesting, but also exchange, ingratiation, and 
sanctions. However, these efforts have not analyzed 
these strategies in terms of speech act types and per- 
locutionary effects so that these strategies might be 
realieed in computational form. 
Finally, research in social psychology has looked 
at how personality traits affect interpersonal interac- 
tion. For example, Kiesler (1983) formulated general 
rules for describing how one person expressing one 
trait (e.g., merciful) can lead to another person ex- 
pressing a symmetric and complementary trait (e.g., 
appreciative). Such interaction dyads are directly 
msppable to the speaker/hearer dyad of speech act 
theory, and the vocabulary of trait terms and pre- 
dictive rules suggest one way of lending organization 
to the great variety of perlocutionary effects. Yet, 
social psychologists have not mapped their general 
trait terms to the classes of speech acts that might 
express these traits. 
What's been lacking is an attempt to integrate 
the lessons learned from these different research ef- 
forts to provide an initial model of social perlocu- 
tions; that is, a model that describes how specific 
speech act types have the potential to produce spe- 
cific effects in a hearer corresponding to a speaker's 
social goals, and that is specified formally enough to 
be used as part of text generation systems. 
3 Our Model 
There are two key questions to address in forming a 
computational model of social perlocutions: 
• What are the possible socially-relevant effects 
of speech acts? 
• What are the relationships between different 
effects? 
3.1 Social Perlocutionary Effects 
We have developed a taxonomy of social perlocution- 
ary effects of speech acts. These effects are defined 
in terms of mental attitudes of the hearer, following 
the assumption in speech act theory that all perlocu- 
tionary effects follow from the hearer's recognition of 
the speaker's communicative intent. The taxonomy 
is: 
1021 
. 
. 
. 
. 
. 
6. 
Beliefs about speaker's precise communicative 
content and communicative intent. 
Beliefs about the speaker's intent to benefit or 
harm the hearer. 
Beliefs about the heater's or speaker's respon- 
sibilities (ascribed or undertaken). 
Beliefs about (or, impressions of) the speaker's 
personality traits. 
The heater's emotions. 
The relationship between the hearer and the 
speaker. 
7. The hearer's goals. 
We developed this taxonomy by reviewing the 
communications studies and social psychology liters- 
ture, as we\]\] as by analysing a corpus of letters and e- 
nudl messages for their speech acts and most promi- 
nent social effects. Prior research on speech acts has 
largely ignored several of these categories, especially 
the effects on personality impressions, emotions, and 
the speaker-hearer relationship. 
3.2 Relationship Between Social Ef- 
fects 
This taxonomy is important because there appear 
to be significant restrictions on the relationships be- 
tween these different classes of effects. 
Figure 2 shows how these different types of effects 
are related. The arrows represent potential causal 
links between effects. These links are potential be- 
cause there are specific conditions associated with 
specific effects that dictate whether one effect will 
cause another. 
Essentially, the effects start with the hearer's 
recognition and acceptance of a message's content 
and culminates in changes to hearer goals and the re- 
latiouship between the hearer and the speaker. That 
is, a speech act directly results in beliefs about the 
content and intent of utterances and these beliefs 
indirectly result in changes to goals, emotions, and 
interpersonal relationships. Specficially, these belief 
can lead to indirect changes in the heater's belief 
about the speaker's intent to benefit or harm the 
hearer, as well as changes to the heater's responsibil- 
ities that involve the speaker. In turn, changes in be- 
lief about whether the speaker intends to benefit or 
harm the header can lead to changes in the hearer's 
goals, the heater's emotions, and the heater's im- 
pressions of the speaker's personality traits. Finally, 
5 
changes in 
H's goals 
6 / 
changes in 
H's emotions 
7 changes to 
the strength of 
H's relationship 
with S 
4changes in 
H's impressions 
f orS'straits 
2 H's belief about 
S's intent to 
benefit or harm H 
H's belief in 
content and 
intent of acts 
directed at H T 
S's speech acts 
directed at H 
3 
changes to 
H's beliefs about 
responsibilities 
involving S 
Figure 2: The Relationships Between Social Effects 
changes to the hearer's emotions can lead to changes 
in the hearer's relationship with the speaker. 
Our hypothesis is that Figure 2 provides a frame- 
work into all speech acts with social effects can be 
mapped. To test this hypothesis, we analyzed in de- 
tail the relationship between the effects of 40 differ- 
ent types of speech acts, and we successfully placed 
each into this framework (Pautler, 1999). These 
speech acts were typical of the letters and messages 
we collected, and they were representative of four of 
the five main categories of speech acts.1 
Figure 3 is an example, showing these effects for 
apolo~zing, a Although not shown in Figure 3, the 
causal relationships between these effects have con- 
ditions attached to them. In Figure 3, for example, a 
condition on an apology leading to the hearer believ- 
ing the speaker feels regret is that the hearer believes 
the speaker is sincere and there is an act for which 
1We did not represent deelar6tions because we chose to 
focus ¢m acts used in casual, interpersonal interactions rather 
thA~ acts that were institutionally framed. 
=We do not rl.;,, that the model applies to groups other 
th~n adu/t Westea'ne~. See Bm'nlund (1989) for comparisons 
en the use of different speech acts by Americana and Japanese. 
1022 
0 
"0 
~° 
0 
H's relationship with 
S is strengthened t 
H's liking for S increases 
H believes Praising 
S is likable 
°. H believes 
Denying 
S is conscientious l ~.. praise 
H believes Warning 
S is accountable 
l ~o ° o .o 
H believes Thanking 
S feels regret 
t 
Apologizing 
Figure 3: The Effects Of Apologising 
an apology is appropriate. 
We draw our terminology for describing specific 
personality traits (e.g., likeable, conscientious) and 
emotions (e.g., gratitude, liking) from existing tax- 
onomies (Kiesler, 1983; Ortony et al., 1988). 
Figure 3 shows effects with arrows leading to 
them from other speech acts, such as praising, warn- 
hag, thanking, and so on. These speech acts are there 
to illustrate that speech acts are related through a 
web of interlocking effects. That is, the causal rela- 
tiouships between speech acts and effects is many- 
to-many: a single act can have many different effects 
and any single effect can be brought about by many 
different acts. For example, expressing a demand 
can bring about compliance, anger, or both, and 
similarly, anger can be caused by a variety of other 
acts, such as issuing a threat. In Figure 3, both 
praising and apologizing are examples of acts that 
can increase the heater's liking for the speaker, and 
both apologiMng and thanking can lead the hearer 
to believe the speaker is accountable. 
This large web of relationships between the ef- 
fects of social speech acts leads to the question: How 
can we efficiently generate the speech acts we need 
to achieve an appropriate emotional response in the 
hearer? 
4 A Model Of Letter Genera- 
tion 
To illustrate the power of our model of social per- 
locution, we have applied it to the task of e-mail 
generation in a system called LetterGen. The sys- 
tem's primary task is to take a high-level commu- 
nicative goal (e.g., inform a colleague that one can't 
attend a meeting) and suggest a set of speech acts 
to achieve that goal. However, once it has made this 
suggestion, the system then interact with the user 
to determine which speech acts will appear in the 
final message and to acquire any additional bat.k- 
ground information needed to iustantiate sentence 
text templates associated with each speech act. 
In addition to the user's explicit input goal, the 
system works with a set of "standardn user goals. 
These goals fall into three classes: 
1. Cost avoidance avoiding undesired aspects of 
a current or incipient situation, such as un- 
wanted social perceptions of oneself. 
2. Status-quo maintenance ~election of an act 
because one of its effects would reinforce a de- 
sired aspect of the current situation (e..g, of- 
feting to help another person because it would 
reinforce one's self-image as a generous per- son). 
3. Trait-based habit--performing of an act as a 
timeworn expression of a personality trait. 
These goals can be thought as a stereotypical model 
of the user (Chin, 1989). These goals are achieved 
opportunistically during the process of determining 
speech acts for the explicitly provided user goal. 
4.1 A Graph-Based Representation 
Of Speech Act Relationships 
LetterGen essentially represents the perlocutionary 
effects of speech acts as a large graph. Figure 4 il- 
lustrates a portion of this representation that relates 
the speech acts of declining, thanking, and apologiz- 
ing. The nodes of the graph represent various effects, 
and the unlabled edges represent a causal relation- 
ships between two effects. There are also constraints 
on when edges can be traversed (although hey are 
1023 
MITIGATES 
I H ,ie ° 1 SIDE EFFECT S is impolite 
t l 
H believes 
S is unappreciative 
EXPLICIT \[ H believes 1 
INITIAL ~ S will not 
GOAL attend 
T 
Declining 
H believes 
S is polite t 
H believes 
S is accountable /\ 
H believes H believes 
S feels S feels regret 
gratitude for 
the offer 
t 
Thanking Apologizing 
Figure 4: A representation for Declining, Thanking, 
and Apologising. 
ward ~ as far as possible. 
5. If an effect is indexed by a mitigates link, fol- 
low the link to the mitigating effect in the 
other chain. Continue with steps 2 and 3. 
As an example, consider the user's communics- 
tive goal to make the hearer believe that the speaker 
will not attend. Lettergen traverses the graph down- 
wards to locate the speech act Declining. After de- 
termining this speech act, LetterGen then traverses 
the graph upward, moving through its effects, veri- 
fying that none of them conflict with known speaker 
goals. In this case, one of the effects of Declining 
conflicts with the speaker's goal that the hearer be. 
lieves the speaker is polite. At this point, LetterGen 
generates a new goal to mitigate that effect, and 
recursively uses its algorithm to locate speech acts 
to achieve that goal. With the failed goal of being 
perceived as polite, LetterGen's downward traver- 
sal locates Thanking and Apologising as appropriate 
speech acts to mitigate that failure. 
not shown in this figure). Finally, there are mitigates 
finks between nodes when two effects are contradic- 
tory. 
A reasonable view of LetterGen's approach is 
that there is a acr/pt associated with each speech act 
that captures the causal chain of effects that poten- 
tlally follow from it. While these effects could pre- 
sumably be determined by reasoning from first prin- 
ciples, these scripts can be viewed as standard meth- 
ogs of achieving communicative goals, and they are 
essentially equivalent to the communicative strate- 
gies proposed by others (Marco, 1997). 
4.2 Determining Appropriate Speech 
Acts 
LetterGen's algorithm for producing a response in- 
volves 5 steps: 
1. Metch the user's goal to one of the nodes (ef- 
fects) in the graph. 
2. From the matching effect, traverse graph finks 
"downward ~ toward the speech act, checking 
the conditions on each link. 
3. For every path that reaches an act by satisfy- 
ing all conditions along the path, add the act 
to the new message by instantiating the act's 
text template. 
4. Detect undesirable side effects of each added 
speech act by traversing all links back "up- 
4.3 An Alternative To Planning 
This approach can be viewed as a form of reactive 
planning. LetterGen can be viewed as having a 
simple goal (communicate a particular belief to the 
hearer), forming a plan (finding a set of speech acts 
that communicate this belief), analyzing the effects 
of the plan (looking for user goals that are violated 
by these speech acts), and opportunistically pursu- 
ing new goals (to mitigate these violations). 
LetterGen differs significantly from most other 
efforts in planning speech acts. These efforts explic- 
itly represent speech acts and their effects as plan 
operators and attempt to synthesize sequences of op- 
erators. Unfortunately, as others have pointed out 
(Cohen and Levesque, 1980; 1990), plan operators 
are not a good representation when acts have long 
chains of effects. That's because each chain that re- 
suits from a given act must be conflated to a fiat 
list of effects, or each effect must be re-envisioned as 
an act, with one operator for each effect and appro- 
priate preconditions so the operators can form the 
appropriate chain. 
LetterGen's approach is most similar to the alter- 
native to planning for speech-modeling proposed by 
Cohen and Levesque (1980, 1990). Their approach 
uses a set of inference rules and act type definitions 
and is explicitly designed to capture sequences of 
this type, 
cl c2 ci 
A(d) ---> El ---> E2 ---..,---> Ei 
1024 
where A(d) is an act that communicates proposi- 
tional content d (definitional content for some act 
type), which induces effect E1 under conditions cl, 
which induces effect E2 under conditions c2, and so 
on. 
This rule formalism is directly mappab\]e to the 
conditionalised causal relations used in our social 
perlocutions model, with two exceptions. One is 
that we capture the rules with an annotated graph 
structure that makes the connectivity among rules 
explicit (scripts). The other provide a specialized 
graph-traversal algorithm that takes advantage of 
key properties of the graph, which allows us to sub- 
stitute et~cient graph traversal for generallsed plan- 
ning. 
5 Implementation 
The current implementation contains a very detailed 
model of speech act effects, containing over 400 
effects and constraints. It is able to generate a 
dozen different types of messages, including initiat- 
ing or terminating a friendship, applying or resign- 
ing from a job, congratulating or consoling someone, 
accepting or declining an invitation, encouraging or 
discouraging someone from doing an act, thanking 
someone, and apologizing to someone. Each of these 
different message types includes an organizational 
template that places generated acts in an appropri- 
ate order for the task. 
An important part of LetterGen is its interac- 
tion with the user. Given a selected message type, 
LetterGen suggests at least three speech acts for the 
user to choose from. For example, the thanking mes- 
sage type (i.e., make them believe you feel gratitude) 
can be instantiated crediting (distributing credit), 
offering (to repay), as well as an overt expression of 
gratitude (i.e., thanking). For each act chosen by 
the user, the system queries the user for the back- 
ground information needed to instantiate an appro- 
priate text template. 
6 Limitations and Future 
Work 
The model currently has three major limitations. 
First, it does not cover all aspects of social inter- 
actions. For example, it does not have conditions 
or effects involving the relative status of the speaker 
and hearer, or specialized roles they might play (e.g., 
judge, employer, and so on). Second, the condi- 
tions on exactly when effects occur need to be elabo- 
rated significantly. Finally, there are socially-related 
speech acts we have not yet represented (e.g., ex- 
pressing sadness, joy, and so on). 
The primary implementation limitation involves 
the background information required to determine 
whether various conditions hold. Currently, the im- 
plementation does not query the user for all the 
background information it could take advantage of. 
The reason is that too many queries makes the pro- 
gram loses its appeal as a work-saving device. A 
related limitation is that its model of the speaker's 
goals is static, rather than dynamic (e.g., the speaker 
is always assumed to have a goal of being polite). We 
are addressing both of these problems by exploring 
techniques for forming a detailed user profile and 
applying across a large set of generated letters. The 
other important limitation is that its organizational 
and text templates are not particularly flexible (e.g., 
they demand a specific speech act order and they 
realize each speech act as a single sentence). One 
way to address this problem is to take the set of 
speech acts that LetterGen wants to generate as a 
goal and to plan exactly how they will be realized 
(Hovy, 1993; Moore and Paris, 1994; Hobbs, 1982). 
One interesting area for future exploration is the 
problem of applying the model to letter understand- 
ing as well as generation. This problem is potentially 
difllcult, as there are a variety of social reasons why 
a particular speech act might have appeared. For ex- 
ample, the thanking act might have been included in 
the example of Figure 1 in order to lessen the social 
debt the invites owes to the inviter, or to avoid in- 
sulting the inviter through curtness, or to make the 
invites feel that he is a polite person, or simply out 
of habit. 
7 Conclusions 
This paper has presented a computational model 
of the sodal perlocutionary effects of speech acts. 
Our model extends previous formal modela of speech 
acts to take into account effects involving emotions, 
impressions, and the interpersonal relationship be- 
tween the speaker and the hearer. In doing so, 
we have integrated earlier results from natural lan- 
guage generation on speech acts, from communica- 
tion studies on communication strategies, and from 
social psychology on how interactions affect person- 
ality traits. 
We have used this model to construct a proto- 
type program that generates letters that meet social 
goals. This task is a key aspect of any general- 
purpose, intelligent, personal assistant that is in- 
1025 
volved in mediating interpersonal interaction. 

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