The ROMPER System: Responding to Object-Related 
Misconceptions using Perspective 1 
Kathleen F. McCoy 
Dept. of Computer and Information Sciences 
University of Delaware 
Newark, De. 19716 
Abstract I 
As a user interacts with a database or expert system, 
s/he may reveal a misconception about the objects modeled 
by the system. This paper discusses the ROMPER system for 
responding to such misconceptions in a domain independent 
and context sensitive fashion. ROMPER reasons about possible 
sources of the misconception. It operates on a model of the user 
and generates a cooperative response based on this reasoning. 
The process is made context sensitive by augmenting the user 
model with a new notion of object perspective which highlights 
certain aspects of the user model due to previous discourse. 
1 Introduction 
A study of transcripts of expert-user dialogues reveals 
that users often exhibit misconceptions about the objects mod- 
eled in a domain. This paper describes the ROMPER system 
(Responding to Object-Related Misconceptions using PERspec- 
tive) which is able to respond to certain classes of these mis- 
conceptions in a principled manner. In doing so the system 
sheds light not only on the process of correcting misconcep- 
tions, but also on issues in natural-language generation, user 
models, and modeling certain contextual effects by a "filtering" 
of the knowledge representation. 
The ROMPER system functions as a part of a natural- 
language interface to a database or expert system. Input to 
ROMPER is a specification that a misconception has been de- 
tected. In this work a miscwnception is defined to be some 
discrepancy between what the system believes (i.e., what is con- 
tained in the system knowledge base) and what the user believes 
(as exhibited through the conversation). The system knowledge 
base includes an object taxonomy and knowledge about object 
attributes and their possible values. 
Several factors may influence the structure and content 
of responses to queries that reveal misconceptions. These in- 
dude the goals of the conversstional participants. If the mis- 
conception is not important to these goals, the response may 
not address the misconception or may address it only mini- 
maliy. ROMPER is concerned with correcting misconceptions 
that are important to the current goals of the conversational 
participants and is thus concerned with generating a maximal 
IMuch of this work wu done while the author wM at the University of 
Pennsylvania and was partially supported by the ARO grant DAA20-84- 
K-0061 and the NFS grant MCS81-07290. 
response. This response is aimed at eliminating the discrepancy 
between what the user believes and what the system believes 
by bringing the user's knowledge into line with the system's. 
This means that the system must not only give the user the 
correct information, but must present it in such a way so as 
to have the user adopt that information. ROMPER has a user 
model available to aid in this task. The user model constitutes 
what the system believes the user believes about the domain. It 
contains the same kind of information as is contained in the sys- 
tem's knowledge base -- an object taxonomy and information 
about objects' attributes and their values. The content of the 
user model, however, may be very different from the content of 
the system's knowledge base. For instance, it may contain less 
information than is contained in the system knowledge base, 
or it may contain some information that is inconsistent with 
the system knowledge base. The user model will not, how- 
ever, contain more information than is contained in the system 
knowledge base since the system is assumed to be an expert in 
the domain. 
In an attempt to respond to a misconception in a natural 
way, the system operates on the model of the user attempting 
to find certain structural configurations which might indicate 
support for the misconception. If one of the configurations is 
found, then a response is generated that refutes the found sup- 
port. ROMPER is specifically concerned with responding to 
two kinds of misconceptions: those involving an object's clas- 
sification (which I call misclassifications) and those involving 
an objects attributes (which I call n~attributlons). Certain 
structural configurations have been identified indicating pos- 
sible support for both kinds of misconceptions. Each identi- 
fied configuration has a response strategy associated with it 
which may be instantiated to respond to the misconception. 
The whole process is made context sensitive by a new notion 
of object perspective which acts to filter the user model, high- 
lighting those aspects which are made important by previous 
dialogue, while suppressing others. The filtering gained by ob- 
ject perspective allows the same misconception by the same user 
to be responded to differently in different contextual situations. 
Output from ROMPER is a formal specification of a re- 
sponse. This specification is then input to the MUMBLE sys- 
tem \[McDS01 which, using a dictionary and grammer supplied 
by Robin Karlin \[Kar85\], produces actual English text. 
97 
2 Misconception Responses 
The view of natural-language generation taken in this 
system is the same as that taken in \[McK82\]. The generation 
process is seen as consisting of two parts: (1) determining the 
content and structure of the response and producing a formal 
message specification, and (2) transforming that specification 
into actual English text. My work has concentrated on deter- 
mining the content and structure of a response to a misconcep- 
tion. It attempts to automate the process of deciding what in- 
formation to include in a response to a misconception by giving 
the system the ability to reason about certain classes of miscon- 
ceptions and typical ways of correcting misconceptions in one 
of the identified classes. This should be contrasted with the a 
priori listing of misconceptions and responses found in most 
existing systems that handle misconceptions (\[SC80\], \[BB78\], 
and \[Woo84\]). 2 
The form of the responses generated by ROMPER de- 
rived from an analysis of transcripts of human conversational 
partners. These transcripts revealed that responses to state- 
ments containing misconceptions often include more than a sim- 
ple denial of the wrong information. This is particularly true in 
circumstances where the misconception is about something im- 
portant to the current goals of the participants. In addition to 
denying the information involved in the misconception, many 
misconception responses include both the corresponding cor- 
rect information, and additional justification for the denial and 
correction given. The justification often involves refuting faulty 
reasoning that may have led the user to the misconception. 
While it may seem that the kinds of faulty reasoning that 
the user may be using to arrive at a misconception are limitless, 
the transcript analysis revealed a surprisingly small number of 
misconception support relations that are refuted by the human 
experts. In addition, these few misconception support relations 
can be couched in terms of a knowledge base (KB) structure 
rather than its content. Thus a system reasoning on a model of 
the user might look for such relations in a domain independent 
fashion. If one is found, information refuting the misconception 
support might be included in the corrective response. 
To see this, let us examine the number of ways that a 
human expert was found to correct one of the misconception 
types handled by ROMPER: misclas~ifications. The strategies 
used by the human experts to respond to a miselassification 
can be exemplified by the number of possible responses to the 
following misconception: 
U. I thought whales were fish. 
R1. No, they are mammals. You may have thought they were 
fish because they are fin-bearing and live in the water. 
However, they are mammals since, (while fish have gills) 
whales breathe through lungs and feed their young with 
2\[Woo84\] represents a departure from the canned response in that she 
is concerned with appropriately structuring a response to reflect a certain 
tutoring style. 
milk. 
R2. No, they are mammals. You may have thought they were 
fish since they are like the fish, sharks, in that both are 
large aquatic creatures and both scare people. However, 
whales are mammals since, (while fish have gills) whales 
breathe through lungs and feed their young with milk. 
R3. No, they are mammals. 
Before analyzing each one of these in detail, let us first 
note their similarities. Each of the above strategies can be seen 
as consisting of three parts: (1) a denial of the incorrect clas- 
sification, (2) a statement of the correct classification, and (3) 
an offering of justification for the denial/correction pair. This 
three part strategy is, in fact, typical of all of the responses 
found in the transcript analysis. Notice that the denial of the 
incorrect information and the offering of the corresponding cor- 
rect information is the same in each of the sample responses 
given. What distinguishes one kind of response strategy from 
another is the kind of justification given in each case. Responses 
R1 and R2 offer two different kinds of justi~cation while R3 of- 
fers no justification. 
Given that examples of each of the above three kinds 
of responses were found in the transcripts, we must ask what 
causes one to be used in preference to another in a particular 
situation. One explanation is that different beliefs about what 
the user believes trigger the use of each strategy. Notice that 
each strategy can be seen as refuting a different kind of support 
for the misconception. My claim is that a speaker may choose 
a strategy depending on the support that he/she believes the 
user may be using to come up with the misconception. Let us 
take each strategy in turn, examine what beliefs might have 
led to the use of that strategy, and then investigate how this 
information might be used by a system to generate responses 
to misconceptions. 
3 Using Response Strategies 
The justification in R1 consists in the expert conceding 
properties that whales have in common with fish (fin-bearing 
and water-living), and overriding that conceded information 
with properties that distinguish whales from fish. The use of 
this strategy by an expert might be explained by the expert be- 
lieving that the user believes that whales and fish are similar, 
and that that similarity may have led to the misclassification. 
An expert having these beliefs might very well find it reason- 
able to concede that the similarity between whales and fish does 
indeed exist, but then go on to show that that similarity is not 
enough to classify whales as fish. S/he may do this, as above, 
by offering properties that make whales mammals instead of 
fish. 
Given that this analysis might explain a human's re- 
sponse to a misconception, we might have a computer system 
98 
adopt this strategy to respond to a misconception in a natu- 
ral way. First, the information included in a response like R1 
can be captured in a response schema as shown below. R1 can 
be seen as an instantiation of this schema where OBJECT is 
instantlated with whale, POSITED with fish, and REAL with 
mammal. The shared attributes are instantiated in the obvious 
way. 
((deny (classification OBJECT POSITED)) 
(state (classification OBJECT REAL)) 
(concede (share-attributes OBJECT 
POSITED 
ATTRIBUTES1) ) 
(override (share-attributes ..... 
POSITED 
ATTRIBUTES2) ) 
(override (share-attributes OBJECT 
REAL 
ATTRIBUTES3) ) ) 
The above schema is called the =like-super" schema be- 
cause it is used by ROMPER when the user exhibits a miscon- 
ception by wrongly classifying some OBJECT as a POSITED 
superordinate and when ROMPER determines that a probable 
reason for the miaclassification is that the user believes that the 
OBJECT and the POSITED superordinate are similar to each 
other. The schema captures a response like R1 by specifying a 
denial of the incorrect classification, a statement of the correct 
classification, and then an offering of justification. The justifi- 
cation is in the form of conceding the similarity that may have 
led to the miaclassification (e.g., the shared attributes), but 
overriding that conceded information with attributes that are 
not shp-ed by the OBJECT and the POSITED superordlnate 
but instead distinguish the two. 
It should be pointed out that this schema encodes two 
kinds of information: a domain-independent specification of the 
content of each proposition included in the response (e.g., an 
object classification or shared attributes between objects), as 
well as information about the rhetorical force or communica- 
tive role played by each proposition (e.g., a denial or state- 
ment or conceded information). The content specification is 
derived from the transcript analysis. The rhetorical force is 
derived from both the transcript analysis and from work done 
by \[McK82\], \[MT83\], and \[Man84\] who have developed theo- 
ries about the role that a proposition can play in a discourse. 
The goal in using such a schema is to have a specification of a 
response that may be filled in with information from the user 
model and that, when instantiated, contains enough rhetori- 
cal information to be turned into a cohesive English text by 
a tactical component. The schema above meets both of these 
requirements. 
The justification included in R2 is also in the form of a 
concede/overrlde pair. However, in the case of R2, rather than 
concede a similarity between whales and al___l fish, a similarity 
between whales and some subset of fish (i.e., the sharks) is 
conceded. The use of this response might be explained by the 
expert believing that the user believes whales and sharks to be 
similar and salient at this point in the discourse. The expert 
might imagine the user to have reasoned: "I don't know how to 
classify whales, but I do know that they are similar to sharks 
and I know that sharks are fish. Perhaps whales are fish as 
well." 
This analysis was used in developing ROMPER by asso- 
ciating a schema based on responses like R2 with a user model 
configuration showing a similarity between the misclassifled ob- 
ject and some descendent of the posited superordinate. The 
schema is termed the "llke-some-super" schema and is shown 
below: 
((deny (classification OBJECT POSITED)) 
(state (classification OBJECT REAL)) 
(concede (similarity 
OBJECT 
DESCENDENT 
(share-attributes OBJECT 
DESCENDENT 
ATTRIBUTESI) ) ) 
(override (share-attributes OBJECT 
REAL 
ATTRIBUTES2) ) ) 
Response R3 can be thought of as the degenerate strat- 
egy since it contains no justification for the denial/correction 
pair. ROMPER instantiates the schema corresponding to R3 
when neither of the two above mentioned knowledge base con- 
figurations can be found in the user model. 
So far this paper has concentrated on mlselassifications. 
ROMPER also handles mlsconceptions involving an object's 
attributes. The transcript analysis revealed three correction 
strategies for misattributions as exemplefied by the following 
responses: 
U. What is the interest rate on this stock? 
R4. Stock doesn't have an interest rate. Were you thinking of 
a bond? 
RS. Stock doesn't have an interest rate. Did you mean divi- 
dend? 
R6. Stock doesn't have an interest rate. 
ROMPER employs three correction schema to handle 
misattributlons; one for each of the response strategies shown. 
R4 can be seen as in instantiation of ROMPER's wrong-object 
schema. This schema offers an object which has the attribute 
involved in the misconception that the user may have either 
confused with the misconception object or made a bad analogy 
from. It is instantiated when an object is found that has the 
attribute involved in the misattribution and is similar to the 
misconception object. 
R5 exemplifies the wrong-attribute schema which offers 
an attribute that the object involved in the misconception does 
99 
have. This response is used when there is reason to beLieve 
the user may have confused the attribute involved in the mis- 
conception with a similar attribute that the object does have. 
ROMPER uses the schema when the misconception object has 
an attribute that is similar to the attribute involved in the mis- 
conception. 
As is the case with the misclassifications, there is a ~de- 
generate" schema for misattributions. This schema contains no 
justification for the correction and is exempLified by R6. 
In summary, a study of transcripts of humans responding 
to misconceptions reveals a great deal of regularity in the way 
misconceptions about objects are corrected. One can abstract 
a small number of response strategies for each of the various 
knowledge base features that might be involved in a misconcep- 
tion. Each of these strategies can be seen as refuting a different 
kind of support that the user may have for the information in- 
volved in the misconception. These strategies are captured as 
schemas in the ROMPER system and each schema is associated 
with a domain independent description of the kind of support 
it refutes. ROMPER, when faced with a misconception, oper- 
ates on a model of the user looking for evidence for one of the 
identified kinds of support. If enough evidence is found, the 
response to the misconception is generated by instantiating the 
corresponding schema. 
4 Effects of Context 
The above section outlined a method for correcting mis- 
conceptions. While the method does seem to be appealing, at 
first glance it seems to have a major flaw. It does not seem 
to take into account the role that previous context plays in 
correcting misconceptions. The responses given by the human 
experts were very context dependent. In two different contexts 
a human expert might choose to correct the same misconcep- 
tion by the same user in two different ways. For example, in 
response to the misconception exhibited by ~I thought whales 
were fish ", an expert might choose R1 in one context and R2 
in another. How can this be explained if the process described 
above is used to respond? 
I claim that the process of correcting misconceptions is 
context sensitive not because the process changes with context, 
but because what the process works on changes with context. In 
particular the piece of the user model that is analyzed in looking 
for possible sources of the misconception changes with context. 
Instead of doing the user model analysis on a flat representa- 
tion containing everything that the user knows at equal levels 
of importance, the analysis is done on a model that has been 
highlighted by previous discourse. Previous discourse serves to 
highlight certain aspects of the user model while suppressing 
others. Different highlighting resulting from different previ- 
ous discourse may cause the user model analysis to conclude 
that different support had been used for the misconception and 
therefore cause a different response strategy to be selected. Ob- 
ject perspective is a notion which can be used to model this 
contextual effect. 
5 Object Perspective 
In this section I introduce a new notion of object per- 
spective as an augmentation to a standard semantic network 
representation. Before introducing this notion let us first ex- 
amine what we want this notion to account for. 
The notion of object perspective has previously been dis- 
cussed in the Literature. It can be Likened to the ~point of view ~ 
one takes on an object in a particular discussion. From a partic- 
ular point of view certain characteristics of the object seem more 
important than others. For instance, a particular building may 
be discussed from the point of view of being someones home on 
the one hand, and from the completely different pont of view 
of being an architectural work on the other. The two different 
views of the same building cause different groups of attributes 
to be important. It is this highlighting of a whole group of at- 
tributes that must be explained. Notice that it could not be 
explained by a focusing mechanism which highlights attributes 
which have been mentioned in the preceding discourse because 
many of the highLighted attributes may not have been expLicitly 
mentioned. What needs to be captured is the feeling that each 
view calls to mind a ~precompiled" set of attributes that seem 
to be important while that view is in effect. 
An attempt to explain this effect has been made by defin- 
ing object perspective as viewing an object as a member of 
one superordinate when, in fact, it may have many superordi- 
nates (\[Gro77\], \[BW77\], and \[TWF*82\]). The highlighting is 
achieved through a limited inheritance mechanism. An object 
inherits only those attributes contributed by the one superordi- 
nate deemed "in perspective". Thus, when a building is viewed 
as an architectural work, for example, it inherits only those at- 
tributes associated with the concept architectural-work in the 
generalization hierarchy. Any attributes that it might inherit 
from other superordinates (e.g., home) are ignored. While this 
notion is intuitively appealing, in practice it .is problematic (see 
\[McC85\] for details) and is unable to handle some additional ef- 
fects which intuitively should be handled by object perspective. 
Two of these effects will be discussed here. 
During the course of a conversation it is usually the case 
that more than one object will be discussed. When this hap- 
pens, usually the same kinds of things are discussed about the 
objects. In essence, a particular highlighting of attributes (or 
point of view) seems to be in force during the conversation. Yet, 
this highlighting is applied to different objects. What seems to 
be happening is that the conversational partners are viewing an 
entire group of objects from the same perspective. This cannot 
be accounted for by the previous definition of object perspective 
unless each of the objects under discussion can be said to have 
the same superordinate. 
A second effect which is not accounted for by the above 
definition, yet seems to hinge on object perspective, has to do 
100 
with the heightened importance of some objects during a dis- 
course. For instance, in the responses R1-R3 above, the correct 
classification of whale was given as mammal. It is the case, how- 
ever, that whales are cetaceans and cetaceans are marnmais. If 
the expert above thought that U. knew about cetaceans, why 
wasn't cetaceans given as the correct classification? Since there 
was no preceding discourse given in this case, some default con- 
text would have to be in force. Apparently, in this context 
cetacean did not seem important enough to mention. Yet in 
other contexts, on can imagine cetacean being given as the cor- 
rect classification even though it had not yet been explicitly 
referred to in the preceding discourse. The importance of the 
object cetacean seems to have something to do with the current 
perspective from which objects are being viewed. The previous 
definitions of object perspective do not address this issue. 
5.1 Perspective: Definition and Representation 
I claim that all of the above criteria can be met by a simple 
notion of object perspective which has the following properties: 
First, instead of tying perspective into the generalization 
hierarchy of objects as has been done in the past, the new notion 
of perspective will be independent of that hierarchy. "Perspec- 
tives" which can be taken on the objects in the domain will be 
defined and will sit in a structure which is orthogonai to the 
generalization hierarchy. 
Second, the number of such perspectives that need be 
defined for the objects in a given domain of discourse is small 
and finite. Moreover, any given domain object may be viewed 
from any one of several perspectives defined for that domain. As 
it turns out, it will make more sense to view some of the objects 
in the domain through some perspectives and not others, but 
this is a feature of perspectives which will be taken advantage 
of later. 
Third, each perspective comprises a set of attributes 
with associated salience values. It is these salience values that 
dictate which attributes are highlighted and which are sup- 
pressed. 
Fourth, one such perspective is designated active at a 
particular point in the discourse. 
This notion of object perspective works as follows. An 
object or group of objects is still said to be viewed through 
a perspective. In particular any object which is accessed by 
the system is viewed through the current active perspective. 
However, instead of dictating which attributes an object in- 
herits, the active perspective affects the salience values of the 
attributes that an object possesses (either directly or inherited 
through the generalization hierarchy). The active perspective 
essentially acts as a filter on an object's attributes - raising the 
salience of and thus highlighting those attributes which have 
a high salience rating in the active perspective, and lowering 
the salience of and thus suppressing those attributes which are 
either given a low salience value or do not appear in the active 
perspective. 
The importance of an object in a discourse is determined 
by the salience values given to the attributes it possesses. The 
idea is that the whole becomes highlighted by having its parts 
highlighted. Thus, during a discussion in which the active per- 
spective highlights many attributes contributed by the object 
"cetacean" in our generalization hierarchy, cetacean will be seen 
as an important object. If, on the other hand, none of the 
attributes associated with cetacean are highlighted, then that 
object will be suppressed. 
This notion of object importance realizes the intuitive 
notion that it makes "more sense" to view some objects through 
particular perspectives than others. It makes more sense to 
view an object through perspectives that highlight many of the 
object's attributes and thereby make the object more domi- 
nant. Notice that we can see a certain amount of symmetry 
here. The perspective determines the salience of an object's 
attributes and the object's importance; the object and its at- 
tributes determines how likely the object is to be viewed from 
a particular perspective. 
5.2 Using Perspective 
A model of a particular domain would include the usual object 
taxonomy containing all of the objects in the domain and all 
of the attributes those objects possess. So in our fish-mammal 
domain we would have sharks as a kind of fish with attributes 
like "scare-people s and "large-aquatic-creature". In addition, 
all of the attributes of fish would also be represented and sharks 
would inherit those attributes as well. 
In addition to the object taxonomy, we must build a sep- 
arate structure containing the perspectives that can be taken on 
the domain objects. One perspective we might imagine defining 
for the fish-mammal domain would be the "body-characteristics" 
perspective. In this perspective attributes like "fin-bearing ~, 
"have-gills", and "breathe-through.lungs ~ would be given high 
salience and thus highlighted. Other attributes would be sup- 
pressed by this perspective. 
Another perspective that might be defined for the fish- 
mammal domain might be the "common-people's-perception" 
perspective. This perspective might highlight attributes like 
"large-aquatic-creatures ~ and *scare-people". Other attributes, 
like "have-gills" and ~fin-bearing" might be suppressed by this 
perspective. 
ROMPER uses the highlighting from object perspective 
in two ways. First, during the user model analysis it uses the 
information to check for user model configurations which might 
indicate particular kinds of support for a misconception. Sec- 
tion 2 introduced two user model configurations which were 
associated with response schemas. The like-super schema was 
associated with a user model configuration that indicated that 
the user believed the misclassified object was like the posited su- 
perordinate. The like-some-super schema was associated with a 
user model configuration that indicated that the user believed 
the misclassified object was like some descendent of the poeited 
101 
superordinate. Notice that both of these user model configu- 
rations hinge on a similarity assessment between objects. The 
similarity metric used by ROMPER is one that is based on the 
objects' common and disjoint attributes which takes attribute 
salience into account \[Tve77\]. This metric will be discussed be- 
low. Since the similarity metric takes attribute salience into 
account and attribute salience is effected by object perspective, 
the active perspective can influence the selection of a miscon- 
ception response schema. 
Second, ROMPER uses the highlighting from object per- 
spective to instantiate the selected response schema. It at- 
tempts to do this using only attributes deemed important by 
the current perspective. 
5.3 Object Similarity 
As was mentioned above, the object similarity metric used by 
ROMPER must be sensitive to context. To date, most AI sys- 
tems that use object similarity use a metric that is based on 
distance in the generalization hierarchy. Such a metric is not 
context sensitive. 
The ROMPER system uses a similarity metric based on 
work done in {Tve77\] which allows contextual information to be 
taken into account. Tversky's metric, called a contrast model, 
is based on the common and disjoint features/propertles of the 
objects involved. Suppose we have two objects a and b where 
A is the set of properties associated with object a and B is the 
set of properties associated with object b. Tversky's measure 
can be expressed as: 
s(a,b) = Of(AN B) - ~f(A - B) - fir(B- A) 
for some 0, o~, and fl > 0. 
In the above equation 0, a, and/3 are parameters which 
alter the importance of each piece of the equation. The function 
f maps over the features and yields a salience rating for each, 
In essence, the contrast model states that the similarity of two 
objects is some function of their common features minus some 
function of their disjoint features. The importance of each par- 
ticulax feature involved (determined by the function f) and the 
importance of each piece of the equation (determined by 0, c~, 
and fl) may change with context. 
In order to use the metric, we must come up with values 
for the functions in the equation. Tversky suggests that the 
0, c~, and fl functions might be affected by the relative promi- 
nence of objects a and b in the discourse. If a is relatively more 
important, then function 0 and a should be greater than/~ re- 
sulting in the attributes of the more prominent object having a 
greater influence over the similarity assessment. While I would 
conjecture that information about the focus of the discourse 
\[Gro81\], \[Sid83\], \[GJW83\] might give an indication of an ob- 
ject's prominence and would therefore be useful in setting the 
values of 0, ~, and /3, in this work I have assumed a value of 
1 for the 0, a, and /3 and have concentrated on setting the f 
function. 
In the ROMPER system the f function has been set us- 
ing the salience values returned after the knowledge base has 
been filtered through object perspective. Using this setting of 
f the same two objects may be seen as very similar when the 
active perspective highlights attributes that the objects have in 
common and suppresses those that are disjoint between them. 
On the other hand, the same two objects may be seen as very 
different when the active perspective suppresses attributes that 
they have in common and highlights those that are disjoint be- 
tween them. 
This similarity metric is used by ROMPER in deciding 
which schema to use to respond to a particular misconception. 
Suppose that ROMPER must respond to the misconception ~I 
thought a whale was a fish" when the active perspective is the 
"body-characteristics ~ perspective defined above. Recall that 
this perspective highlighted attributes like fin-bearing, have- 
gills, and breathe-through-lungs. Under this perspective, at- 
tributes common to whales and all fish are highlighted. Using 
a Tversky-like similarity metric this highlighting causes whales 
and fish to be seen as similar. ROMPER would thus respond 
using the like-super schema producing a response similar to R1. 
If, on the other hand, the same misconception were 
encountered when the perspective was "common-people's- 
perception", the attributes that whales and all fish have in com- 
mon would not be highlighted. Rather, attributes llke scare- 
people and large-aquatic-creatures shared with just a subset of 
fish, the sharks, would be highlighted. Under these conditions, 
the similarity metric would return a low similarity rating for 
whales and all fish (and thus the "like-super ~ schema would 
not be applicable), but a high similarity rating for whales and 
sharks. Thus, the "like-some-super" schema would be used to 
produce a response similar to R2 above. 
One can imagine how other perspectives might make nei- 
ther the "like-super" nor the ~like-some-super" schemas appli- 
cable, causing the "no-support" schema to be used. 
5.4 Choosing the Active Perspective 
In order for the notion of object perspective to be truly bene- 
ficial, there must be a mechanism for choosing the active per- 
spective based on previous discourse. While this topic is still 
very much open to investigation, some preliminary research has 
revealed several factors that might influence the choice of active 
perspective. 
Perhaps one of the most influential pieces of information 
useful in choosing a perspective is the user's current goal. In 
\[MWM85\] the user's goal completely determines which perspec- 
tive is active. In their work each perspective which can be taken 
on the domain objects is indexed by potential goals. Thus, once 
the system has determined what the user's goal probably is, it 
has also determined what perspective the user has probably 
taken on the domain objects. 
While it is true that the user's goal is a good source 
of information to use to determine the probable perspective, 
102 
other factors may also influence this choice. These include the 
attributes and objects mentioned so far in the dialogue. The 
mentioned attributes are obviously thought to be important 
and one would therefore expect them to be given a fairly high 
salience rating in the active perspective. Thus, the choice of 
active perspective can be narrowed dow~ to those in which the 
mentioned attributes appear with high salience. 
By the same token~ the objects mentioned so far in the 
dialogue can also give a clue concerning the active perspec- 
tive. One would expect that the active perspective would deem 
these objects important. Therefore the system might look for 
perspectives that give high salience ratings to many of the at- 
tributes associated with objects that have been mentioned in 
the discourse. 
In this section I have identified several factors which in- 
fluence the choice of active perspective. This choice, however, 
is a question which remains as an open research topic. Still 
unanswered are questions such as: When does a perspective 
change? How long is a perspective active? Is there a rela- 
tionship between a discourse unit \[GS85\] and perspective? Is 
there any structure to the space of perspectives that would put 
constraints on moving from one active perspective to another? 
These questions must be taken up in future research on per- 
spective. 
5.5 An Example 
In this section an example is given which indicates how 
the choice of perspective influences how a misconception may 
be corrected. Recall that in correcting a misattribution one of 
the correction schemas used by ROMPER called for a similar 
object to be offered as a possible object of confusion. A study of 
transcripts reveals, however, that this schema may be instanti- 
ated in different ways depending on the context. Consider the 
following dialogue: 
U. I am interested in investing in some securities to use as 
savings instruments. I want something short-term and I 
don't have a lot of money to invest so the instrument must 
have small denominations. I am a bit concerned about the 
penalties for early withdrawal. What is the penalty on a 
T-bill? 
S. Treasury Bills don't have a penalty. Were you thinking of 
a Money Market Certificate? 
In this case the money market certificate was seen as 
being similar to the treasury bill and therefore included in the 
response. A different object might be used in a different context. 
Consider: 
U. I am interested in investing in some securities. Safety is 
very important to me, so I would probably like to get 
something from the government. I am a bit concerned 
about the penalties for early withdrawal. What is the 
penalty on a T-bill? 
S. Treasury Bills don't have a penalty. Were you thinking of 
a Treasury Bond? 
The difference in these two responses can be explained 
by different perspectives begin taken on the objects. Suppose 
that our knowledge base contains the following objects and at- 
tributes in the financial securities domain. 
Money Market Certificates 
Maturity: 3 months 
Denominations: $1,000 
Issuer: Commercial Bank 
Penalty for Early Withdrawal: 10% 
Purchase Place: Commercial Bank 
Safety: Medium 
Treasury Bills 
Maturity: 3 months 
Denominations: $1,000 
Issuer: US Government 
Purchase Place: Federal Reserve 
Safety: High 
Treasury Bond 
Maturity: 7 years 
Denominations: $500 
Issuer: US Government 
Penalty for Early Withdrawal: 20% 
Purchase Place: Federal Reserve 
Safety: High 
The following perspectives might be reasonable for the domain 
(here we are assuming salience values from low salience of 0 to 
high salience of 1): 
Savings Instruments 
Maturity - 1.0 
denominations - 1.0 
safety - 0.5 
Issuing Company 
issuer - 1.0 
safety - 1.0 
purchase-place - 0.5 
Notice that the perspective of Savings Instruments highlights' 
maturity and denominations, and somewhat highlights safety. 
This indicates that when people are discussing securities as sav- 
ings instruments, they are most interested in how long their 
money will be tied up and in what denominations they can 
save their money. The perspective of Issuing Company, on the 
other hand, highlights different attributes. When securities are 
discussed from this perspective, things like who the company is 
and how stable an investment in the company is, become im- 
portant. Other attributes of the securities are ignored (recall 
that attributes not mentioned in the perspective get assigned a 
low salience rating). 
103 
• I 
Consider how perspective might effect the misconception 
response. Given the discourse preceding the utterance contain- 
ing the misconception in our first dialogue, it is reasonable to 
assume that the perspective of "Savings Instruments" is the 
active perspective at the time of the misconception utterance. 3 
A system attempting to respond to this misconception might 
proceed by attempting to instantiate the wrong object schema 
described above. Recall that this schema is applicable when 
there is a similar object which has the property involved in the 
misconception. The system might collect all objects which have 
the attribute in question and then test their similarity with the 
object involved in the misconception. In our knowledge base 
there are two objects which have the attribute involved in the 
misconception: Money Market Certificates and T-Bonds. 
Suppose the attributes of these objects were assigned the 
salience values given by the Savings Instrument perspective. 
Applying the Tversky metric using the salience values attached 
by this perspective (and assuming a value of 1 for ~,al and fl) 
we get: 
s(T-Bill, P~4-Cert) = f(maturity, denom) - f(safety) 
= 2 - .8 = 1.8 ===> high similarity 
s(T-Bill, T-Bond) = fCsafety) -f(maturity, denom) 
= .5 -2 = -I.5 ===> low similarity 
With these calculations the system would choose the 
Money Market Certificate as the possible object of confusion 
and respond: 
S. Treasury Bills don't have a penalty. Were you thinking of 
a Money Market Certificate? 
Contrast the above calculations with calculations that 
might occur given a different active perspective. The discourse 
preceding the misconception utterance in the second example 
suggests the active perspective of "Issuing Company". Using 
the salience values attached by this perspective the similarity 
metric would produce the following calculations: 
sCT-Bill, ~ Cert) 
= f() - f(issuer, safety, purchase) 
= 0 - 2.5 = -2.5 ===> low similarity 
s(T-Bill, T-Bond) 
= f(issuer, safety, purchase) -fO 
-- 2.5 - 0 = 2.5 ===> high similarity 
in this case a reasonable response by the system would be: 
S. Treasury Bills don't have a penalty. Were you thinking of 
a Treasury Bond? 
As the examples show, changes in the active perspective 
can account for the same misconception begin responded to in 
two different ways. 
SROMPER does not calculate the active perspective. Instead, it is input 
to the system. 
6 Conclusion 
If we want our natural-language front-ends to database 
or expert systems to mimic human behavior, they must have 
the ability to handle misconceptions. This paper has described 
a methodology for handling object-related misconceptions and 
has illustrated this methodology on misconceptions involving 
object misclassificatlons. 
The proposed method for responding to object-related 
misconceptions requires associating response schemas with cer- 
tain structural configurations of the user model. The response 
schemas described in this paper were derived from a corpus of 
transcripts and were associated with user model configurations 
that would explain their use by a human expert in responding 
to a misconception. 
A system might use the pairing of strategies to configu- 
rations upon encountering an object-related misconception by 
searching the user model for one of the identified configurations. 
If one was found, the associated schema could be instantiated 
to generate a corrective response. 
The context-dependent nature of responses to miscon- 
ceptions is accounted for not by having the pracess of correct- 
ing misconceptions change with context, but rather by having 
what the process tnorks on change with context. A new notion 
of object perspective was introduced as an augmentation to a 
flat semantic network representation of the user. Object per- 
spective provides a highlighting of the user model as a result of 
previous discourse. This resulting user model was shown suffi- 
cient for accounting for different responses being given to the 
same misconception in different situations. 
7 Acknowledgements 
I would like to thank my advisors, Aravind Joshi and 
Bonnie Webber, for their many helpful comments throughout 
the course of this work. Special thanks also go to Sandra Car- 
berry and Martha Pollack for their comments on various drafts 
of this paper. 
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