MODELING EXTEMPORANEOUS ELABORATION 
Marie A. Bienkowski 
Bell Communications Research 
445 South Street, MRE 2P358 
Morristown, NJ 07960-1961, USA 
ABSTRACT 
Intelligent problem solving systems must be able to ex- 
press their results in a coherent and flexible manner. One 
way this can be done is by eztemporaneous elaboration, the 
method of language production that underlies more skilled 
tasks such as explanation. This paper outlines a computa- 
tional model for extemporaneous elaboration that is imple- 
mented in a computer model called Extemper, shows ex- 
amples of its operation, and compares it with other models of 
language production. Extemper contains the four com- 
ponents minimally required for elaboration: 1) an efficient 
method for linearizing a knowledge structure, 2) a 
translation/selection mechanism for producing a conceptual 
textbase from the knowledge structure, 3) local coherence 
operators which provide local connections between textbase 
elements, and 4) a conceptual generator to translate the 
coherent textbase into English. 
Introduction 
An intelligent problem solving system may be required to 
participate in a purposive discourse with a user. Purposive 
discourse is dialogue where a welbdefined goal is pursued in a 
stereotyped and efficient - but not inflexible - way. Many 
common types of purposive discourse that could occur with 
an intelligent system, such as tutoring, explaining, and advis- 
ing, require lengthy elaborations. Automated reasoners or 
problem solvers, in particular, are often required to explain, 
describe, or justify, that is, to elaborate on, their solutions. 
The process of producing unrehearsed and unedited exposi- 
tions during purposive discourse is called ezternporaneous or 
spontaneous elaboration. 
This paper describes a computational model for extem- 
poraneous elaboration implemented in a computer model 
called Extemper. The model consists of four components. 
The first is a linearizer which provides the overall structure 
of the elaboration by either a) following a trace of the 
processing of a problem solver (rehashing) or, b) directing a 
rehearsal of the knowledge used by the system (i.e., instan- 
tinting the knowledge structures normally used for problem 
solving for use in language production). The second com- 
ponent is a set of selectors which determine what will be ex- 
pressed based on considering the type of discourse, the dis- 
course goals of the listener, and the relevance of domain- 
specific items. 
The linearizer and selectors produce the overall concep- 
tual form of the elaboration. It is given a coherent linguistic 
form by the combination of local coherence operators, which 
examine conceptual forms to track focus, add connective 
words, etc., and a sentence generator (Cullingford, 1986) 
which renders it in English. Extemper's core model of 
elaboration, then, consists of four components: a linearizer, 
selectors, local coherence operators, and a sentence gener- 
ator. These constitute a minimal cognitive architecture for 
elaboration: minimal because more may be needed for other 
elaboration types (e.g., a component to build and maintain a 
listener model for tutoring), and cognitive because com- 
ponents like these have been proposed by psychologists for 
models of language production (e.g., vanDijk and Kintsch, 1083). 
In the next section, I discuss how Extemper's components 
cooperate to produce an elaboration. In Section 3, I describe 
the type of domain that Extemper's minimal architecture is 
sufficient for: descriptions of the operations of a problem sol- 
ver. That section also distinguishes rehearsal from rehashing; 
these are different ways of obtaining the knowledge to be 
elaborated. Before concluding, I compare Extemper with 
other systems designed for text and discourse production. 
Extemper: A Computational Model 
Extemper implements a four component, minimal cog- 
nitive architecture for elaboration to produce descriptions of 
the behavior of problem solving systems. The problem solv- 
ing systems it interacts with are a route'planner and an 
academic counseling system. 1 Extemper's linearizer, in 
cooperation with one of these problem solvers, provides a 
succession of pieces of the knowledge source being elaborated 
to translators. These meaning-preserving translators 
produce conceptual forms which are linked into a conceptual 
knowledge base and then processed by the selectors. The 
output of both the translators and selectors is represented 
using a version of the Eclectic Representations for 
Knowledge Structures (ERKS) frame representation system 
developed by Cullingford (1986). 
The concept selection cycle builds a single ERKS meaning 
structure until meaning structure (ms) functions determine 
that a newly selected concept does not fit with the current 
one. The ms functions use a heuristic evaluation of the con- 
nectedness of propositions to determine when to generate a 
sentence. They rely on the semantic classes of concepts and 
the goal structure represented in the conceptual knowledge 
base. For example, concepts representing simple actions 
taken by the route planner are considered to be connected, 
and can be expressed in a single sentence if they occur con- 
secutively. Actions that contribute directly to the same goal 
are also considered to be connected. Note that there may 
also be syntactic constraints on what may fit into one sen- 
tence (e.g., Derr and McKeown, 1984, describe how con- 
siderations of focus may force complex sentences to be 
generated) but those constraints would not apply at this 
level, since the syntactic form the meaning structure will 
take is not yet known. 
After concept selection, the local coherence operators 
mediate between the conceptual form of the elaboration and 
its syntactic form by annotating meaning structures with in- 
structions for the sentence generator. These instructions in- 
crease the connectedness or local coherence of the sentences 
to be generated; given and new information, for example, is 
computed at this level. The annotated meaning structures 
are stored in a textbase whose elements are translated into 
English by the sentence generator. The sentence generator 
operates by repeatedly looking up words to span conceptual 
forms and ordering the words and those concepts that were 
not spanned according to predicates stored with the words. 
Additionally, there are conceptual "sketchifiers" that alter or 
delete information to express a concept more succinctly. 
Two important contributions can be found in this com- 
putational model for language production (in addition to its 
description of the minimal components needed for 
1Extemper and the problem solving systems are imple- 
mented in Frans Lisp on a Pyramid 90x. 
191 
elaboration). One contribution is illustrated by Extemper's 
use of the knowledge embodied in an intelligent system for 
guiding an elaboration. Extemper follows the principle that 
if a natural ordering exists for a body of knowledge, it should 
be used to guide extemporaneous elaboration. A natural or- 
dering is one that corresponds to some common sense use of 
the knowledge, such as a problem solver might exhibit. 
Another contribution of this elaboration model is that it 
demonstrates that a language production mechanism can be 
designed to operate primarily on conceptual, language-free 
forms. Manipulations are performed on the conceptual form 
of an elaboration, not the linguistic form. This is possible in 
Extemper because information from the problem solver is 
converted into conceptual form by the meaning-preserving 
translators, and the selectors, local coherence operators, and 
sentence generator operate only on these forms. Use of a 
conceptual representation throughout the elaboration process 
makes possible a blurring of the distinction (traditionally 
made in language production systems) between =what to 
say = and "how to say it.= 
Linearizatlon and Translation 
When a topic is chosen for elaboration, the concepts to be 
communicated must be selected in an appropriate order. 
Extemper's linearizer is responsible for presenting pieces of a 
knowledge structure for selection in an order that is derived 
in some fashion from information inherent in the structure 
itself. To gain access to this ordering information, the 
linearizer must cooperate closely with the intelligent system 
that is producing or manipulating the knowledge. I discuss 
this further in Section 3 while describing the problem solving 
systems whose behavior Extemper describes. 
Orderings can be achieved in three general ways: exploit- 
ing a given ordering, imposing an ordering, or deriving an or- 
dering. In exploiting a given ordering, the connections al- 
ready present in the knowledge structure are followed to find 
the next piece of information to say. An ordering must be 
imposed if the knowledge structure contains no ordering in- 
formation or if a different ordering is desired. An imposed 
ordering is usually something that is known to be an effective 
means of ordering (e.g., describing visual scenes by salience). 
Deriving an ordering must be done in cases where the under- 
lying knowledge does not contain ordering information (and 
such information would be expensive to compute) and no im- 
posed ordering exists that can achieve the goals of the 
elaboration. 
The strategy Extemper uses is to exploit the common 
sense ordering inherent in a knowledge structure. The prin- 
ciple that knowledge should be elaborated in a manner 
similar to the way it is used is based on the observation that 
complex transformations of knowledge are not found in 
naturally occuring elaborations, presumably because they are 
too time consuming to produce. However, even though Ex- 
temper uses a common sense ordering as the primary order- 
ing method, it can be overcome by other factors such as 
pragmatic goals and listener input (although this has not 
been done for any of the implemented examples). Also, the 
design of Extemper's lineariser does not preclude the use of 
imposed structures for elaboration. In such cases, the 
linearizer follows an external structure instead of the one in- 
ternal to the knowledge structure used by the reasoner. This 
flexibility is possible because Extemper's linearizer is imple- 
mented as a set of agenda-based tasks. Because these tasks 
are preemptable, so is the operation of the linearizer, in keep- 
ing with Extemper's eventual use in a discourse processing 
system. 
Exploiting a given ordering does not guarantee a com- 
plete ordering. A planner, for example, may arbitrarily 
choose which parts of a plan should be pursued first if the 
parts independently achieve a goal. The linearizer must, in 
these cases, decide what parts to express first. This is done 
using rules that choose among several possible next things to 
say. For example, in the route planning domain, a goal may 
be shown to be true simply by reasoning about it, for ex- 
ample, =I am on Olden Street because I am in E-Quad and 
E-Quad is on Olden Street." and "I assumed you were at 
Green Hall because you wanted me to meet you there." 
These two reasoning chains may together contribute to the 
satisfaction of a goal. Neither one takes precedence over 
another, so the linearizer uses a rule which prefers the shorter 
of the two (the second one). Another rule, given several sub- 
goals for a goal, will choose the ones that are supported by 
reasoning chains over ones that are supported by further 
planning. Supplementing the ordering produced by the 
problem solver with (common sense) rules like these gives a 
complete ordering of concepts for the selectors. 
Selection 
Relevance is an important constraint on discourse produc- 
tion. Constraints on relevance may be imposed by the over- 
all type of discourse, pragmatic goals, and the characteristics 
of the domain knowledge. Determining if a given piece of 
knowledge is relevant is considerably more difficult than 
selecting a correct ordering for a knowledge structure. Tech- 
niques that have been explored include annotations on 
perceptual-level representations of scenes to indicate visual 
salience (Conklin, et.ai., 1983), and databases containing 
multiple indexes keyed on user point of view (McKeown, 
et.ai., 1985). However, these techniques involve supplement- 
ing static knowledge bases and do not readily apply to select- 
ing pertinent information from traces of a problem solver 
(which Extemper needs for both rehearsal and rehashing). 
Instead, Extemper uses selectors that rely on different 
relevance factors to assist in determining relevance Uon the 
fly." 
The selectors that Extemper uses are rules constrained by 
the overall discourse goal of the listener. This enables Ex- 
temper to produce elaborations that are tailored to a par- 
ticular goal (this is the only global relevance factor used in 
the current implementation). The operation of the selectors 
is based on the ERKS method for representing meaning, 
namely that meaning representations consist of a kernel 
(main or core concept) and nuances (ancillary concepts that 
distinguish similar meaning structures). For example, "I 
drove to Green Hall." is distinguished from =I traveled to 
Green Hall." by the nuance that, in the second case, the 
vehicle of transportation was a car. Decomposing meaning in 
this way allows meaning structures to be built piecewise by 
the selectors. 
Selectors can only perform actions that influence the con- 
ceptual content of ERKS meaning structures. They can be 
divided into three categories: 1) those that select infor- 
mation for inclusion in a meaning structure, 2) those that 
modify the selected information, and 3) those that add ancil- 
lary information to meaning structures. For example, a 
selector for the route planner might choose to include a con- 
cept representing a goal restatement, e.g., =I determined that 
I could meet you at Green Hall if you and I were there." 
Another selector (depending upon the overall discourse goal 
the listener is presumed to have) might choose to modify the 
concept to omit mention of how this conclusion was arrived 
at (i.e., omit =I determined that=.) A selector that adds an- 
cillary information might produce introductory sentences like 
the following from the academic counseling domain: "There 
are four major requirements you must meet for Liberal Arts 
and Sciences. = 
The selectors add concepts to one meaning structure until 
the next concept to be added no longer fits with the ones al- 
ready selected. In this way, complex sentences are built from 
separate concepts. The separate concepts are connected ac- 
cording to the relationships found in the knowledge structure 
(e.g., consecutive actions are joined with a temporal 
connective). Prior to sentence generation, however, more in- 
192 
formation must be added to the ERKS meaning structure. 
Local Coherence Operators 
The local coherence operators annotate ERKS meaning 
structures to create ties between successive parts of the 
elaboration. Based on examination of the conceptual forms 
in the current and previous meaning structures, they make 
decisions that may influence the syntactic form of the sen- 
tence. The most common one is the computation of given 
and new information, which affects focus. For example, in 
the route planning domain, if a sentence mentions walking to 
a place, that (given) location may be the focus for the next 
sentence. 
I walked to Green Hall. 
At Green Hall, I checked my mail. 
Another common operation is adding connective words, e.g., 
using =Ok" to signal the end of a description of how a goal 
may be achieved in the academic counseling domain. 
Local coherence operators are also used to provide certain 
default values for the meaning structure by filling 
annotation nuances. The most useful of these nuances are 
shorthand forms for full concepts that express specifications 
that are extrinsic to the meaning of the kernel concept: ab- 
solute time, relative time, and modals. These nuances should 
be filled by the problem solver, but are not because the 
problem solvers used do not reason about time or modal in- 
formation. The English sentence generator needs this infor- 
mation, so it has to be added in this ad hoc manner. 
The result of the first three parts, linearization, selection, 
and local coherence operations, is a set of generation instruc- 
tions represented as a conceptual form (consisting of a kernel 
and major nuance concepts) plus annotations on it. This 
ERKS meaning representation is entered into a teztbase, a 
representation of the contents of the discourse. After being 
added to the textbase, the sentence generator produces 
English text from it in the manner described in Section 1. 
Further details on the sentence generator can be found in 
Cullingford (1986). 
The Problem Solving Domain 
The emphasis in Extemper's design has been on modeling 
elaborations of the knowledge and behavior of goal-directed 
problem solving systems. These problem solvers are typified 
by systems such as route planners or spatial reasoners. They 
are of interest because they solve commonplace problems, 
and their solutions can be described by commonplace (hence 
extemporaneous) elaborations. 
Two basic categories of elaborations are needed for 
problem solvers, and Extemper produces both kinds. One 
provides a reAasAing of something the problem solver has 
done from a trace of its execution. Rehashed elaborations 
say what was done to solve the problem, why it was done, 
and what the results were. The other type of elaboration 
uses the problem solver's knowledge base directly to 
elaborate on reAear6ed knowledge. The elaborator controls a 
rehearsal (instead of following an execution trace) by alter- 
nately rehearsing knowledge and elaborating on it. (Here, 
rehearsing means fleshing out knowledge structures, e.g., by 
filling in the value of variables.) Rehearsal is useful for 
providing information without the overhead of running the 
problem solver. 
Extemper serves as a first step for investigations into 
methods for producing a variety of elaborations. The 
problem solving elaborations it produces are descriptions of 
processes or actions based on the domains of route planning 
(rehashed elaborations) and academic counseling (rehearsed 
elaborations). These two domains use very different types of 
knowledge, from rigid and script-like (the curriculum 
knowledge in the academic counseling domain) to a mixture 
of goals, rules and plans (the route planning knowledge). 
Extemper's ability to interpret these different types of 
domain knowledge demonstrates its flexibility. 
Table 1 shows a sample elaboration produced by Ex- 
temper. This description of the route planner's behavior is 
intended for an operator or programmer of the route planner, 
and is highly detailed. It gives the justifications underlying 
the route planner's actions and the "mental states" (e.g., 
determined, a~6umed) of the planner. Extemper also 
produces two less verbose elaborations from the same 
knowledge structure, based on different views of the dis- 
course goal of the listener. One of them is shown in Table 2 
(the other is simply the last sentence in the elaboration 
shown in Table 2). In the first, the listener is assumed to be 
unfamiliar with the area being described, in the second, a 
high degree of familiarity is assumed. (The output lacks 
fluency because the focus of this work was not on sentence 
generation. Some improvement in the output could be 
gained from more work on sentence generation and 
pronominalization.) 
I determined that I could meet you at GreenHall 
if you and I were there. 
I assumed you were at GreenHall because you 
wanted me to meet you there. 
I knew I could be at.GreenHall if I was on 
the street that it was on, faced it, and 
walked to it. 
GreenHall was on Washington so I wanted to 
be on Washington. 
I knew I could be on Washington if I was on 
the street that intersects wlth Washington 
and was near the street that I was on, 
faced Washlngton° and walked to the 
intersection of Washington and the street 
that intersects with Washington and was 
near the street that I was on. 
William was the street that intersects 
with Washlngton so I wanted to be on 
William and to walk to the intersection of 
Nashington and ¥1111am. 
I assumed I was on William because I was at 
EQuad and EQuad was on William. 
I assumed I faced WashlnEton because WashlnEton 
was oriented west of EQuad, I was at EQuad, 
and I faced west. 
I walked to the intersectlon of WashlnEton and 
Wllllam. 
I knew I could face GreenHall if I turned in 
the direction that i turn in to be oriented 
towards GreenHall. 
I turned right and walked to GreenHall. 
Table 1: A Verbose Elaboration. 
.............................................. 
193 
I could meet you at Green Hall if you and I 
were there. 
Washington was the street that GreenHall was on 
so I wanted to be on Washington. 
William was the street that intersects with 
Washington so I wanted to be on William and to 
walk to the intersection of Washington and 
William. 
I walked to the intersection of Washington and 
William. turned right, and walked to GreenHall. 
Table 2: A Less Verbose Elaboratlon. 
Related Work 
The focus of much current research on language produc- 
tion is on text and discourse planning. Even though Ex- 
temper models an extemporaneous process, it must generate 
lengthy text like text production systems, and be as flexible 
as discourse planning models in interacting with a user. 
HELPCON (a precursor of Extemper) used the conceptual 
generator CGEN to generate instructions for s computer- 
aided design system (Cullingford, 1982; Bienkowski, 1983). 
I-IELPCON extended CGEN's notion of sketchification at the 
concept level to the knowledge structure (KS) level by using 
KS sketchifiers corresponding to different links in "j'eature 
script8 • that described the CAD tool. HELPCON would 
traverse a feature script, apply the KS sketchifiers to it, and 
send the remaining concepts to CGEN. Extemper's use of 
exploited structure is similar to this. 
McKeown's TEXT system (1985) uses =discourse 
strategies = to link communicative purposes (e.g., derme, 
compare) with the appropriate set of rhetorical techniques 
(e.g., identify, contrast) for realizing them. The strategies 
are represented as recursive schemata which, along with the 
immediate focus of the discourse, impose a partiM ordering 
on the techniques in a given strategy. Schemata are hard to 
find for some knowledge structures so a more knowledge- 
driven approach, such as Extemper uses, is needed in some 
cases. 
Mann and Moore's (1081) system, KDS, used a fragment 
(break input representation into proposition-sised pieces) and 
compose (combine the resulting propositions) method for 
generating text from a semantic net representation. The ag- 
gregator that KDS uses for combining wastes effort trying 
useless combinations, a method which could cause problems 
for large texts. In similar work, Vaughan (1986) has 
proposed to use a plan and critique cycle to produce text. 
For modeling extemporaneous language production, however, 
critiquing is too time~consuming, and more reliable planning 
mechanisms are needed. 
Discourse planning systems treat language as the result of 
execution of speech acts that are designed to affect another's 
beliefs or knowledge. These speech acts are planned by 
modeling their effects on a model of the other's beliefs and 
knowledge. Appelt (1082), for example, views language 
production as one of several modaiities for a planner of ac- 
tions. His planner, KAMP, explicitly manipulates assertions 
involving intensional concepts such as believe and know. II- 
locutionary acts such as "inform = are axiom=tired to capture 
the intentionality behind them; this enables KAMP to reason 
about how it can realize its intentions. 
Extemper, like discourse planners, integrates the general 
knowledge an intelligent system has with its language be- 
havior. Discourse planning methods, however, are not ap- 
plicable to this work because planning an entire elaboration 
by reasoning about how to affect another's knowledge would 
overwhelm any planner. Also, reasoning about speech acts 
does not solve basic problems in elaboration production, since 
the main relevant speech act for elaboration is only 
"inform. = A planner, rather, would have to reason about 
how to achieve ordering, relevance and coherence goals in an 
elaboration. Previous planning systems, then, have operated 
on a different planning level than is needed for elaborators 
like Zxtemper. 
The alternative to "planning from scratch = methods is to 
use schemata or scripts for lengthy discourse production. 
While these certainly are part of an expert speaker's "bag of 
tricks, = there are several problems with relying on them ex- 
clusively for a process like elaboration. One problem is that 
some elaborations, such as those involving close interaction 
with a problem solver that is dynamically computing the 
knowledge structure to be elaborated, cannot be described by 
schemata. Another is that schema may not be needed for 
some tasks, if the knowledge structure is sufficiently well- 
structured. 
Extemper does not offer a complete solution to either the 
problem of making elaboration as flexible as discourse or the 
problem of finding efficient ways to generate long texts. 
However, by making use of the knowledge that a problem 
solving system has, Extemper can guide an elaboration, and 
its reliance on a flexible task execution methodology 
(described in Bienkowski, 1986) to do this leaves open the 
possibility that a method for reasoning about discourse goals 
can be added in future revisions. 
Summary and Conclusions 
Extemper's reliance on a reasoning system to guide its 
processing has been emphasized. In promoting a tight in- 
tegration between language processing and reasoning com- 
ponents, this work is similar to the work on integrating pars- 
ing and memory described by Schank, 1980. Interfacing to a 
reasoning system requires a translation capability, however, 
so Extemper does not (in general) generate directly from the 
structures that the reasoner uses. But it cooperates closely 
enough so that, if the actions of the reasoner are regarded as 
the =thoughts= of the machine, Extemper serves as a window 
on those thoughts. 
Extemper relies entirely on conceptual forms throughout 
its processing cycle. The exclusive use of conceptual forms 
has been pushed as far as possible to see if the =semantics" 
(or pragmatics) of a sentence could justify its =syntax. = 
Sometimes this works; other times, nothing in a conceptual 
form suggests why a particular syntactic form should be 
needed. This results in non-fluent English. This is not to say 
that there is no reason for using the forms that could in- 
crease fluency, but that the reasons are to be found else- 
where. The work of McKeown (1985) on how tracking focus 
can affect syntactic structures, is an example of this. 
Extemper represents a minimal architecture for extem- 
poraneous elaboration as purposive discourse. The four com- 
ponents it uses are necessary for any elaboration system. 
They are also sufficient for a certain class of knowledge 
sources. This work contributes to research on language 
production by both identifying extemporaneous elaboration 
as a naturalistic ability that is worthy of study, and by 
proposing a computational model for it. 
194 
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