UNDERSTANDING PRAGMATICALLY ILL-FORMED INPUT 
FL Sandra Carberry 
Department of Computer Science 
University of Delaware 
Newark, Delaware 19711 USA 
ABSTRACT 
An utterance may be syntactically and semant- 
Ically well-formed yet violate the pragmatic rules 
of the world model. This paper presents a 
context-based strateEy for constructing a coopera- 
tive but limited response to pragmatlcally ill- 
formed queries. Sug~estlon heuristics use a con- 
text model of the speaker's task inferred from the 
preceding dialogue to propose revisions to the 
speaker's ill-formed query. Selection heuristics 
then evaluate these suggestions based upon seman- 
tic and relevance criteria. 
I INTRODUCTION 
An utterance may be syntactically and semant- 
ically well-formed yet violate the prasmatlc rules 
of the world model. The system will therefore 
view it as "ill-formed" even if a native speaker 
finds it perfectly normal. This phenomenon has 
been termed "pragmatic overshoot" \[Sondheimer and 
Weischedel,1980\] and may be divided into three 
classes: 
\[ I\] User-specifled relationships that do 
exist in the world model. 
\[2\] 
not 
EXAMPLE: "Which apartments are for 
sale?" 
In a real estate model, single apart- 
ments are rented, not sold. However apart- 
ment buildings, condominiums, townhouses, and 
houses are for sale. 
User-specified restrictions on the relation- 
ships which can never be satisfied, even with 
new entries. 
EXAMPLE: "Which lower-level English 
courses have a maxim,-, enrollment of at 
most 25 students?" 
In a University world model, it may be 
the case that the maxim,-, enrollments of 
This material is based upon work supported by the 
National Science Foundation under grants IST- 
8009673 and IST-8311400 
lower-level English courses are constrained 
to have values larger than 25 but that such 
constraints do not apply to the current 
enrollments of courses, the maximum enroll- 
ments of upper-level English courses, and the 
maximum enrollments of lower-level courses in 
other departments. The sample utterance is 
pragmatically ill-formed since world model 
constraints prohibit the restricted relations 
specified by tbe user. 
\[3\] User-specifled relationships which result in 
a query that is irrelevant to the user's 
underlying task. 
EXAMPLE: "What is Dr. Smlth ' s home 
address?" 
The home addresses of faculty at a 
university may be available. However if a 
student wants to obtain special permission to 
take a course, a query requesting the 
instructor's home address is inappropriate; 
the speaker should request the instructor's 
office address or phone. Although such 
utterances do not violate the underlying 
domain world model, they are a variation of 
pragmatic overshoot in that they violate the 
listener's model of the speaker's underlying 
task. 
A cooperative partlc/pant uses the informa- 
tion exchanged during a dialogue and his knowledge 
of the domain to hypothesize the speaker's goals 
and plans for achieving those goals. This context 
model of goals and plans provides clues for inter- 
preting utterances and formulating cooperative 
responses. When pragmatic overshoot occurs, a 
human listener can modify the speaker's ill-formed 
query to form a similar query X that is both mean- 
ingful and relevant. For example, the query 
"What is the area of the special weapons 
mag~azine of the Alamo?" 
erroneously presumes that storage locations have 
an AREA attribute in the REL database of ships 
\[Thompson, 1980\] ; this is an instance of the first 
class of pragmatlc overshoot. Depending upon the 
speaker's underlying task, a listener m/ght infer 
that the speaker wants to know the REMAINING- 
CAPACITY, TOTAL-CAPACITY, or perhaps even the 
LOCATION (if "area" is interpreted as referring to 
"place") of the Alamo's Special Weapons Magazine. 
In each case, a cooperative participant uses the 
preceding dialogue and his knowledge of the 
200 
speaker to formulate a response that ~.%ght provide 
the desired information. 
This paper presents a method for handling 
this first class of pragmatic overshoot by formu- 
lating a modified query X that satisfies the 
speaker's needs. Future research may extend thls 
technique to handle other pragmatic overshoot 
classes. 
Our work on pragmatic overshoot processing is 
part of an on-going project to develop a robust 
natural language interface \[Weischedel and Son- 
dhetmer, 1983\]. Mays\[1980\], Webber and 
Nays\[1983\], and Ramshaw and Welschedel\[1984\] have 
suggested mechanisms for detecting the occurrence 
of pragmatic overshoot and identifying its causes. 
The ms.ln contribution of our work is a context- 
based strategy for constructing a cooperative but 
llm~ted response to pragmatically ill-formed 
queries. This response satisfies the user's per- 
ceived needs, inferred beth from the preceding 
dialogue and the ill-formed utterance. In partic- 
ular, 
\[i\] A context model of the user's goals and plans 
provides expectations about utterances, 
expectations that may be used to model the 
user's goals. We use e context mechanism 
\[Carberry, 1983\] to build the speaker's 
underlying task-related plan as the dialogue 
progresses and differentiate between local 
and global contexts. 
\[23 Only alternative queries which mis~ht 
represent the user's intent or at least 
satisfy his needs are considered. Our 
bvDothesls is that the user'a lnferred plan, 
~bythecontextmodel, ~Jtggg4Lt,~ 
substitution for the ZL ~ causln~ the 
overshoot. 
II KNOWLEDGE REPRES~TATION 
Our system requires a representation for each 
of the following: 
\[i\] 
\[2\] 
\[3\] 
\[,\] 
the set of dome/n-dependent plans and goals 
the speaker,s plan inferred from the preced- 
ing dialogue 
the existing relationships among attributes 
and entity sets in the underlying world model 
the semantic difference of attributes, rela- 
tions, entity sets, and functlon~ 
Plans are represented using an extended 
STRIPS \[Fikes and Nilsson, 1971\] formalism. A plan 
can contain subgoals and actions that have associ- 
ated plans. We use a context tree \[Carberry, 
1983\] to represent the speaker's inferred plan as 
constructed from the preceding dialogue. Nodes 
within this tree represent goals and actions which 
the speaker has investlgated;these nodes are des- 
cendants of parent nodes representing higher-level 
goals whose associated plans contain these lower- 
level actions. The context tree represents the 
global context or overall plan inferred for the 
speaker. The focused plan is a subtree of the 
context tree and represents the local context or 
particular aspect of the plan upon which the 
speaker's attention is currently focused. This 
focused plan produces the strongest expectations 
for future utterances. 
An entity-relationship model states the pos- 
sible primitive relationships among entity sets. 
Our world model includes a generalization hierar- 
chy of entity sets, attributes, relations, and 
functions and also specifies the types of attri- 
butes and the dome/ns of functions. 
III CONSTRUCTING THE CONTEXT MODEL 
The plan construction component is described 
in \[Carberry, 1983\]. It hypothesizes and tracks 
the changing task-level goals of a speaker during 
the course of a dialogue. Our approach is to 
infer a lower-level task-related goal frsm the 
speaker,s explicitly comaunlcated goal, relate it 
to potential hi~er-level plans, and build the 
complete plan context as the dialogue progresses. 
The context mechanism distinguishes local and glo- 
bal contexts and uses these to predict new speaker 
goals from the current utterance. 
IV PRAGMATIC OVERSHOOT PROCESSING 
Once pragmatic overshoot has been detected, 
the system formulates a revised query QR request- 
ing the lnformatlon needed by the user. Our 
hypothesis is that the user's inferred plan, 
represented by the context model, suggests a sub- 
stitution for the proposition that caused the 
pragmatic overshoot. The system then selects from 
amongst these suggestions using the criteria of 
relevance to the current dialogue, semantic 
difference from the proposition in the user's 
query, and the type of revision operation applied 
to this proposition. 
A. Su~stion 
The suggestion mechanism examines the current 
context model and possible expansions of its con- 
stituent goals and actions, proposing substitu- 
tions for the proposition causing the pragmatlc 
overshoot. This erroneous proposition represents 
either a non-exlstent attribute or entity set 
relationship or a function applied to an inap- 
propriate set of attribute values. 
The suggestion mechanism applies two classes 
of rules. The first class proposes a simple sub- 
201 
atitution for an attribute, entity set, relation, 
or function appearing in the erroneous proposi- 
tion. The second class proposes a conjunction of 
propositions representing an expanded relatlon~ip 
path as a substitution for the user-specifled 
propositlo~ These two classes of rules may be 
used together to propose both an expanded rela- 
tionship path .and an attribute or entity set sub- 
stitution. 
I. SimD~-Substitution Rules 
Suppose a student wants to pursue an indepen- 
dent study project; such projects can be directed 
by full-time or part-time faculty but not by 
faculty who are "extension" or "on sabbatical". 
The student might erroneously enter the query 
"what is the classificatioD of Dr. Smith?" 
Only students have classification attributes (such 
as Arts&Science-1985, Engineerlng-1987); faculty 
have attributes such as rank, status, age, and 
title. Pursuing an independent study project 
under the direction of Dr. Smith requires that Dr. 
Smith's status be "full-time" or "part-time". If 
the listener knows the student wants to pursue 
independent study, then he might infer that the 
student needs the value of this status attribute 
and anger the revised query 
"What is the status of Dr. Smith?" 
The suggestion mechanic, contains five simple 
substitution rules for handling such erroneous 
queries. One such rule proposes a substitution 
for the user-specifled attribute in the erroneous 
propositio~ Intuitively, a listener anticipates 
that the speaker will need to know each entity and 
attribute value in the speaker's plan inferred 
from the domain and the preceding dialogue. Sup- 
pose this inferred plan contains an attribute ATTI 
for a member of ENTITY-SETI, namely ATTI(ENTITY- 
SETI ,attribute-value), and that the speaker 
erroneously requests the value of attribute ATTU 
for a member entl of ENTITY-SETI. Then a coopera- 
tive listener might infer that the value of ATTI 
for entity entl will satisfy the speaker's needs, 
especially if attributes ATTI and ATTU are closely 
related. 
The substitution mechanism searches the 
user's inferred plan and its possible expansions 
for propositions whose arguments unify with the 
arguments in the erroneous proposition causing the 
pragmatic overshoot. The above rule then suggests 
substituting the attribute from the plan's propo- 
sition for the attribute specified in the user's 
query. This substitution produces a query 
relevant to the current dialogue and may capture 
the speaker's intent or at least satisfy his 
needs. 
2. ExDanded Path Rules 
Suppose a student wants to contact Dr. Smith 
to discuss the appropriate background for a new 
seminar course. Then the student might enter the 
query 
"What is Dr. Smith's phone number?" 
Phone numbers are associated with homes, offices, 
and departmental offices. Course discussions with 
professors may be handled in person or by phone; 
contacting a professor by phone requires that the 
student dial the phone number of Dr. Smith,s 
office. Thus the listener might infer that the 
student needs the phone number of the office occu- 
pied by Dr. Smith. 
The second class of rules handles such "miss- 
ing logical Joins". (This is somewhat related to 
the philosophical concept of "deferred ostenalon" 
\[Qulne,1569\].) These rules apply when the entity 
sets are not directly related by the user- 
specified relation RLU--- but there is a path R 
in the entity relationship model between the 
entity sets. We call this path expansion since by 
finding the missing Joins between entity sets, we 
are constructing an expanded relational path. 
Suppose the inferred plan for the speaker 
includes a sequence of relations 
R1 (ENTITY-SETI ,~TITY-SETA) 
R2 ( ENTITY-SETA, ~ TITY-SETB) 
R3(ENTITY-SETB, ~TITY-SET2) ; 
then the listener anticipates that the speaker 
will need to know those members of ~TITY-SETI 
that are related by the composition of relations 
RI ,R2,R3 to a member of EIqTITY-SET2. If the 
speaker erroneously requests those members" of 
ENTITY-SETI that are related by ~ (or alterna- 
tively RI or R3) to members of ~TITY-SET2, then 
perhaps the speaker really meant the expanded path 
RImR2*R3. The path expansion rules suggest sub- 
stituting this expanded path for the user- 
specified relation. 
We employ a user model to constrain path 
expansion. This model represents the speaker's 
beliefs about membership in entity sets. If prag- 
matic overshoot occurs because the speaker misused 
a relation 
R(ENTITY-SETI, ~TITY-SET2) 
by specifying an argument that is not a member of 
the correct entity set for the relation, then path 
expansion is permitted only if the user model 
indicates that the speaker may believe the errone- 
ous argument is not a member of that entity set. 
EXAMPLE: "Which bed is Dr. Brown assigned?" 
Suppose beds are assigned to patients in 
a hospital model. If Dr. Brown is a doctor 
and doctors cannot simultaneously be 
patients, then path expansion is permitted if 
our user model indicates that the speaker may 
recognize that Dr. Brown is not a patient. 
In this case, our expanded path expression 
may retrieve the beds assigned to patients of 
Dr. Brown, if this is suggested by the 
inferred task-related plan. 
202 
To limit the components of path expressions 
to those relations which can be meaningfully com- 
bined in a given context, we make a strong assump- 
tion: that the relations comprising the relevant 
expansion appear on a single path within the con- 
text tree representing the speaker's inferred 
plan. For example, suppose the speaker's inferred 
plan is to take C-$105. Expansion of this plan 
will contain the two actions 
Learn-From-Teacher- In-Cl ass( SPEAKER, 
se ction, faculty) 
such that Teach( faculty, section) 
Obtain-Necessary-Extra-Help( SPEAKER, 
section, teaching-asslstant) 
such that Assists(teaching-assistant, section) 
The associated plans for these two actions specify 
respectively that the speaker attend class at the 
time the section meets and that the speaker meet 
with the section's teaching assistant at the time 
of his office hours. Now consider the utterance 
"When are teaching assistants available?" 
A direct relationship between teachinE assistants 
and time does not exist. The constraint that all 
components of a path expression appear on a single 
path in the inferred task-related plan prohibits 
composing Assists(teachlng-asslstant,sectlon) and 
Meet-Time(sectlon, tlme) to suggest a reply con- 
sisting of the times that the CSI05 sections meet. 
S. ~~cha~sm 
The substitution and path expansion rules 
propose substitutions for the erroneous proposi- 
tion that caused the pragmatic overshoot. Three 
criteria are used to select frnm the proposed sub- 
stitutions the revised query, if any, that is most 
likely to satisfy the speaker's intent in making 
the utterance. 
First, the relevance of the revised query to 
the speaker's plans and goals is measured by three 
factors: 
\[i\] A revised query that interrogates an aspect 
of the current focused plan is most relevant 
to the current dialogue. 
\[2\] The set of higher level plans whose expan- 
sions led to the current focused plan form a 
stack of increasingly more general, and 
therefore less immediately relevant, active 
plans to which the user may return. A 
revised query which interrogates an aspect of 
an active plan closer to the top of this 
stack is more expected than a query which 
reverts back to a more general active plan. 
\[33 Within a given active plan, a revised query 
that investigates the single-level expansion 
of an action is more expected, and therefore 
more relevant, than a revised query that 
investigates details at a much deeper level 
of expsnsion. 
Second, we can classify the substitution 
T-->V which produced the revlsed query into four 
categories, each of which represents a more signl- 
flcant, and therefore less preferable, alteration 
of the user's query (Figure I). Category I con- 
tains expanded relational paths R11P.?S... mRn such 
that the user-speclfied attribute or relation 
appears in the path expression. For example, the 
expanded path 
Treats( Dr. BrOwn, patient) Wls- Assigned( patient, room) 
is a Category I substitution for the user- 
specified proposition 
Is- Assigned( Dr. Brown, rotz~) 
SUBSTITUTION 
CATEGORY TERM T 
Expanded relational path 
including the user-specifled 
attribute or relation 
Attribute, relation, entity 
set, or function semantically 
similar to that specified 
by the user 
Expanded relational path, 
including an attribute or 
relation semantically similar 
to that speclfled by the user 
Double substitution: entity 
set and relation semantically 
similar to a user-speclfled 
entity set and relation 
SUBSTITUTION 
VARIABLE V 
User-speclfled attribute 
or relation 
User-specified attribute, \[ 
relation, entity se~, or 
function 
User-specifled attribute 
or relation 
User-specified entity set\[ 
and relation I 
I I 
Figure I. Classification of Query Revision Operations 
203 
contained in the semantic representation of the 
query 
"Which bed is Dr. Brown assigned?" 
Category 2 contains simple substitutions that 
are semantically similar to the attribute, rela- 
tion, entity set, or function specified by the 
speaker. An example of Category 2 is the previ- 
ously discussed substitution of attribute "status" 
for the user specified attribute "classification" 
in the query 
"What is the classification of Dr. Smith?" 
Categories 3 and 4 contain substitutions that 
are formed by either a Category I path expansion 
followed by a Category 2 substitution or by two 
Category 2 substltutlons. 
Third, the semantic difference between the 
revised query and the original query is measured 
in two ways. First, if the revised query is an 
expanded path, we count the number of relations 
comprising that path; shorter paths are more 
desirable than longer ones. Second, if the 
revised query contains an attribute, relation, 
function, or entity set substitution, we use a 
generalization hierarchy to semantically compare 
substitutions with the items for which they are 
substituted. Our difference measure is the dis- 
tance from the item for which the substitution is 
being made to the closest common ancestor of it 
and the substituted item; small difference meas- 
ures are preferred. In particular, each attri- 
bute, relation, function, and entity set ATTRFENT 
is assigned to a primitive semantic class: 
PRIM-CLASS( ATTRFENT , CLASSA) 
Each semantic class is assigned at most one 
immediate auperclass of which it is a proper sub- 
set : 
SUPER( CLASSA, CL ASSB) 
We define function f such that 
f(ATTRFENT , i+1) = CL~.SS 
if PRIM-CLASS( ATTRFENT, CLASSal ) 
and SUPER( CLA$Sal, CLASSa2) 
and SUPER( CLASSa2, CLASSaS) 
and ... 
and SUPER( CLkSSal, CLASS) 
If a revised query proposes substituting 
ATTRFENTnew for ATTRFENTold, then 
semantl c#difference ( ATTRFEN Tnew, ATTRFEN Told) 
=NIL if there does not exist j,k such that 
f( ATTRFEN Tnew, j) =f( ATTRFENTold, k) 
=mln k such that there exists j such that 
f( ATTRFEN Tnew, j) =f( ATTRFEN Tol d, k) 
otherwise 
An initial set is constructed conslstil~g of 
those suggested revised queries that interrogate 
an aspect of the current focused plan in the con- 
text model. These revised queries are particu- 
larly relevant to the current local context of the 
dialogue. Members of this set whose difference 
measure is small and whose revision operation con- 
sists of a path expansion or simple substitution 
are considered and the most relevant of these are 
selected by measuring the depth within the focused 
plan of the component that suggested each revised 
query. If none of these revised queries meets a 
predetermined acceptance level, the same selection 
criteria are applied to a newly constructed set of 
revised queries sug~sted by a higher level active 
plan whose expansion ied to the current focused 
plan, and a less stringent set of selection cri- 
teria are applied to the original revised query . 
~et. (The revised queries in this new set are not 
immediately relevant to the current local dialogue 
context but are relevant to the global context.) 
As we consider revised queries suggested by higher 
level plans in the stack of active plans 
representing the global context, the acceptance 
level for previously considered queries is 
decreased. Thus revised queries which were not 
rated hilly enough to terminate processing when 
first suggested may eventually be accepted after 
less relevant aspects of the dialogue have been 
investigated. This relaxation and query set 
expansion is repeated until either an acceptable 
revised query is produced or all potential revised 
queries have been consldered. 
V EX~.MPLF~ 
Several examples are provided to illustrate 
the suggestion and selection strategies. 
\[I\] Relation or Entity Set Substitution 
"Which apartments are for sale?" 
In a real-estate model, single apart- 
ments are rented, not sold. However apart- 
ment buildings, condc~ini,-,s, townhouses, and 
houses are for sale. Thus the speaker's 
utterance contains the erroneous proposition 
For-Sale(apar tment) 
where apartment is a member of entity set 
APARTMENT. 
If the preceding dialogue indicates that 
the speaker is seeking temporary living 
arrangements, then expansion of the context 
model representing the speaker's inferred 
plan will contain the posslble action 
Rent( SPEAKER, apartment) 
such that For-Rent(apartment) 
The substitution rules propose substituting 
relation For-Rent frc~ this plan in place of 
relation For-Sale in" the speaker's utterance. 
On the other hand, if the preceding 
dialogue indicates that the speaker 
represents a real estate investment trust 
interested in expanding its holdings, an 
204 
expansion of the context model representing 
the speaker's inferred plan will contain the 
possible action 
Purchase( SPEAE~B, apartment-building) 
where apartment-buildlng ls a member of 
entity set APARTmeNT-BUILDING. Purchasing an 
apartment building necessitates that the 
bttllding be for sale or that one convince the 
owner to sell It. Thus one expansion of this 
Purchase plan includes the precondition 
For-Sale(apartment-bullding) 
The substitution rules propose substituting 
entity set APABT~NT-BUILDING from thls plan 
for the entity set APABT~NT in the speaker's 
utterance. 
\[2\] Function Substitution 
"What is the average rank of CS faculty?" 
The function AVEBAGE cannot be applied 
to non-numerlc elements such as "professor". 
The speaker's utterance contains the errone- 
ous proposition 
AVERAGE( rank, fn- value) 
such that Department-Of(faculty,CS) 
and Bank( faculty, rank) 
If the preceding dialogue indicates that the 
speaker is evaluating the C~ department, then 
an expansion of the context model represent- 
lng the speaker's lnferred plan wlll contain 
the possible action 
Evaluate-Faculty( SPEAKER, CS) 
The plan for Evaluate-Faculty contains the 
action 
Evaluate( SPEAKER, ave-rank) 
such that ORDERED-AVE( rank, ave-rank) 
and Department-Of( faculty, CS) 
and Bank( faculty, rank) 
If a domain D of non-numeric elements has an 
explicit ordering, then we can associate wlth 
each of the n dome.ln elements an lndex number 
between 0 and n-1 speclfylng its poaltlon in 
the sorted domain. The function ORDERED-AVE 
appearing In the speaker's plan operates upon 
non-numeric elements of such domains by cal- 
culating the average of the index numbers 
associated wlth each element instead of 
attempting to calculate the average of the 
elements themselves. The substitution rules 
propose substituting the function ORDERED-AVE 
from the speaker's inferred plan for the 
function AVERAGE in the speaker's utterance. 
ORDERED-AVE and AVERAGE are semantically 
similar functions so the difference measure 
for the resultant revised query will be 
emall. 
\[3\] Expanded Relational Path 
"when does Mltchel meet?" 
A university model does not contain a 
relation mET between FACULTY and TI~S. 
H~ever, faculty teach courses, present sem- 
inars, chair ooamlttees, etc., and courses, 
seminars, and committees meet at scheduled 
times. The speaker's utterance contalns the 
erroneous proposition 
Meet- Tlme( Dr. Mt tchel, time) 
If the preceding dialogue indicates that 
the speaker is considering taking CSI05, then 
an expansion of the context model represent- 
ing the speaker's inferred plan will contain 
the action 
Earn-Credi t- In-Sectl on( SPEAKER, section) 
such that Is-Sectlon-Of(section, CS105) 
Expansion of the plan for Earn-Credlt-ln- 
Section contains the action 
Learn-From- Teacher- In-C1 ass( SPE AKEB, 
section, faculty) 
such that Teach( faculty, section) 
and the plan for thls action contains the 
action 
At tend-Cl ass( SPEAKER, place, time) 
such that Meet-Plave(sectlon, place) 
and Meet- Time( section, time) 
The two relations Teach(Dr.~fltchel,sectton) 
and Meet-Time( section, time) appear on the 
• same path in the context model. Therefore 
the path expansion heuristics suggest the 
expanded relational path 
Teach( Dr. Mi tchel, section) "Meet-Time( ae ctlon, time) 
as a substitution for the relation 
Meet- Time( Dr. Mi tchel, time) 
in the user's utterance. Only one arc Is 
added to produce the expanded relational path 
and it contains the user-specifled relation 
Meet-Time, so the difference measure for this 
revlsed query ls small. 
VI BELATED WORK 
Erlk Mays\[1980\] discusses the recognition of 
pragmatic overshoot and proposes a response con- 
talnlng a llst of those entity sets that are 
related by the user-speclfied relation and a llst 
of those relations that connect the user-speclfled 
entity sets. Houever he does not use a model of 
whether these pos~ibllltles are applicable to the 
user's underlying task. In a large database, such 
responses will be too lengthy and include too many 
irrelevant alternatives. 
205 
Kapl an\[ 1 979\], Chang\[ 1 97 8\] , and Sowa\[ 1 976\] 
have investigated the problem of missing Joins 
between entity sets. Kaplan proposes using the 
shortest relational path connecting the entity 
sets; Chang proposes an algorithm based on minimal 
spanning trees, using an a priori weighting of the 
arcs; $owa uses a conceptual graph (semantic net) 
for constructing the expanded relation. None of 
these present a model of whether the proposed path 
is relevant to the speaker's intentions. 
VII LIMITATIONS ~ND FUTURE WORK 
Pragmatic overshoot processing has been 
implemented for a domain consisting of a subset of 
the courses, requirements, and policies for stu- 
dents at a University. Our system ass,s, es that 
the relations comprising a meaningful and relevant 
path expansion will appear on a single path within 
the context tree representing the speaker's 
inferred plan. This restricts such expansions to 
those communicated via the speaker's underlying 
inferred task-related plan. However this plan may 
fall to capture some associations, such as between 
a person's Social Security Number and his name. 
This problem of producing precisely the set of 
path expansions that are meaningful and relevant 
must be investigated further. Other areas for 
future work include: 
\[I\] Extensions to handle relationships among more 
than two entity sets 
\[2\] Extensions to the other classes of pragmatic 
overshoot mentioned in the introduction. 
\[3\] Extensions to detect and respond to queries 
which exceed the knowledge represented in the 
underlying world model. We are currently 
assuming that the system can provide the 
i r2ormation needed by the speaker. 
VIII CONCLUSIONS 
The main contribution of our work is a 
context-based strategy for constructing a coopera- 
tive but limited response to pragmatically ill- 
formed queries. This response satisfies the 
speaker's perceived needs, inferred both from the 
preceding dialogue and the ill-formed utterance. 
Our hypothesis is that the speaker's inferred 
task-related plan, represented by the context 
model, suggests a substitution for the proposition 
causing the pragmatic overshoot and that such 
suggestions then must be evaluated on the basis of 
relevance and semantic criteria. 
ACKNOWLEDGMENTS 
I would like to thank Ralph Weischedel for 
his encouragement and direction iD this research 
and for his suggestions on the style and content 
of this paper and Lance Ramshaw for many helpful 
discussions. 
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