ENHANCING EXPLANATION COHERENCE WITH RHETORICAL STRATEGIES 
MARK T. MAYBURY 
Rome Air Development Center 
Intelligent Interface Group 
Griffiss AFB, Rome NY 13441-5700 
maybury@radc-tops20.arpa 
and 
Cambridge University Computer Laboratory 
Cambridge, England CB2 3QG 
ABSTRACT 
This paper discusses the application of a 
previously reported theory of explanation 
rhetoric (Maybury, 1988b) to the task of 
explaining constraint violations in a hybrid 
rule/frame based system for resource 
allocation (Dawson et al, 1987). This 
research illustrates how discourse strategies 
of explanation, textual connectives, and 
additional justification knowledge can be 
applied to enhance the cohesiveness, 
structure, and clarity of knowledge based 
system explanations. 
INTRODUCTION 
Recent work in text generation includes 
emphasis on producing textual presentations 
of the explanations of reasoning in 
knowledge-based systems. Initial work 
(Swartout, 1981) on the direct translation of 
underlying system knowledge led to insights 
that more perspicuous justifications would 
result from keeping track of the principles or 
deep causal models which supported that 
knowledge (Swartout and Smoliar, 1988). 
And experiments with discourse strategies 
demonstrated the efficacy of the rhetorical 
organization of knowledge to produce 
descriptions, comparisons (McKeown, 1985) 
and clarification (McCoy, 1985). Researchers 
have recently observed (Paris et al, 1988) that 
the line of explanation should not 
isomorphically mirror the underlying line of 
reasoning as this often resulted in poorly 
connected text (Appelt, 1982). Others have 
attempted to classify patterns of explanations 
(Stevens and Steinberg, 1981; Schank, 
1986). The approach presented here is to 
exploit generic explanation strategies and 
focus models (Sidner, 1983; Grosz and 
Sidner, 1988) to organize the back-end 
justification via an explanation rhetoric -- that 
is, a rhetorical model of strategies that 
humans employ to persuade, support, or 
clarify their position. The result is a more 
connected, flowing and thus easier to follow 
textual presentation of the explanation. 
KNOWLEDGE REPRESENTATION 
and EXPLANATION 
Previous research in natural language 
generation from knowledge based systems 
has primarily focused on independent 
knowledge representation schemes (e.g rule, 
frame or conceptual dependency formalisms). 
In contrast, the application chosen to test the 
concepts of rhetorical explanations is an FRL 
(Roberts and Goldstein, 1977) based mission 
planning system for the Air Force which 
utilizes both rules and frames during 
decision-making. Hence, the explanations 
concern rule-based constraint violations 
which result from inference about entities in 
the knowledge base, their attributes, and 
relationships. For example, if the user plans 
an offensive counter air mission with an 
incompatible aircraft and target, the system 
will automatically signal a constraint violation 
via highlighting of objects on the screen. If 
the user mouses for explanation, the system 
will state the conflicting rule, then list the 
supporting knowledge, as shown in figure 1. 
- 168 - 
The choice for AIRCRAFT is in question because: 
BY TARGET-AIRCRAFr-1:1 THERE IS A SEVERE CONFLICT BETWEEN 
TARGET AND AIRCRAFT FOR OCA10022 
1. THE TARGET OF OCA1002 IS BE307033 
2. BE30703 RADIATES 
3. THE AIRCRAFT OF OCA1002 IS F-111E 
4. F- 111E IS NOT A F-4G 
Figure 1. Current Explanation of Rule Violation 
The weak textuality of the presentation 
manifests itself through ungrammatical 
sentences and the implicit suggestion of 
relationships among entities, placing the 
burden of organization upon the reader. 
Moreover, it lacks essential content that 
specifies why an F-111E is not acceptable. 
That "F- 111E IS NOT A F-4G" makes little 
contribution to the justification, and at best 
implicitly suggests an alternative (an F-4G). 
generated with templates followed by a direct 
translation of the explanation audit trail (a 
trace of the inferences of the constraint 
propagation algorithm as shown in figure 2). 
The explanation trace is of the form: 
(rule-constraint (justification-knowledge-type 
((justification-content) (support-code))*)*)* 
where * means 1 to N repetitions. In the 
example, the rule constraint is TARGET- 
((TARGET-AIRCRAFT- 1 
(DATA (TARGET OCA1002 BE30703)) 
(INHERITANCE (IS-A BE30703 ELECTRONICS)) 
(DATA (AIRCRAFF OCA 1002 F- 111 E) 
((NOTEQ F-111E (QUOTE F-4G)))))) 
Figure 2. Audit Trail of 
One reason the text lacks coherence is 
because it fails to specify precise 
relationships among introduced entities. 
This can be achieved not only by sequential 
order, but through the use of models of 
rhetoric, textual connectives, and discourse 
devices such as anaphora and pronominal 
modifiers. For instance, rather than achieving 
organization from some model of naturally 
occurring discourse, the presentation is 
isomorphic to the underlying inference chain. 
In figure 1, the first two sentences are 
1This is the name of the rule. 
2Reads "Offensive Counter Air Mission 1002". 
3Reads "Battle Element number 30703". 
Constraint Failure 
AIRCRAFF- 1, and the two justification types 
are DATA and INHERITANCE, representing 
knowledge and relationships among entities 
in the FRL knowledge base. Notice that the 
(AIRCRAFT OCA1002 F-111E) tuple is 
followed by a lisp code test for inequality of 
F-111E and F-4G aircraft. It is unclear 
(indeed unspecified) in this formalism that the 
reason for this test and the preference for an 
F-4G is its ability to handle search radar. 
Thus, discrimination of the two aircraft on 
the basis of structure, function, capability or 
some other characteristic would further 
clarify the explanation. Therefor, there is a 
need not only for linguistic processing to 
enhance the coherence of the presentation in 
figure 1, but also additional knowledge to 
enhance the perspicuity of the explanation. 
- 169 - 
EXPLANATION RHETORIC 
The implemented system, EXPLAN, 
exploits models of rhetorical strategies, focus 
models, as well as entity-distinguishing 
knowledge to improve the organization, 
connectivity and surface choices (e.g. 
connectives and anaphor) of the text. The 
system first instantiates a pool of relevant 
explanation propositions from both the 
explanation audit trail as well as from the 
knowledge base as both are sources of 
valuable clarifying information. The text 
planner uses a predicate selection algorithm 
(guided by a global and local focus model, 
knowledge of rhetorical ordering, 
relationships among entities in the knowledge 
base, and the explanation audit trail) to select 
and order propositions which are then 
realized via a case semantics, a relational 
grammar, and finally morphological 
synthesis algorithms (Maybury, 1988a). 
In our example, the first task is to 
determine the salience of entities to the 
explanation. The generator includes the 
current frame (that is, the current mission 
being planned, OCA1002) in the global focus 
of attention. However, global focus also 
must include those slots which may have 
relevance to constraint violations. Figure 3 
shows the OCA1002 mission frame which 
has many slots, only a few of which are 
central to the explanation, namely the 
AIRCRAFT and TARGET slots. A selection 
algorithm filters out semantically irrelevant 
slots (e.g. AIO, DISPLAY) and retains slots 
trapped by the constraint violation. Salient 
objects in the knowledge base are marked, 
including the parent and children of the 
object(s) in question (which are explicitly in 
focus) and the siblings or cousins of the 
global focus (which are implicitly in focus). 
After selecting the global focus 
(OCA1002, AIRCRAFT, and TARGET), 
and marking salient objects in the knowledge 
base, the planner selects three propositions 
from the instantiated pool guided by the local 
focus model and the model of explanation 
discourse. The proposition pool includes 
previously reported (McKeown, 1985) 
rhetorical types such as attributive, 
constituent, and illustration, but also includes 
a wide range of justificatory rhetorical 
predicate types such as characteristic, 
componential, classificatory, physical-causal, 
generalization, associative, and functional, as 
reported in (Maybury, 1988b). 
These predicates are grouped into sub- 
schema as to whether they identify the 
problem, support the identification or 
diagnosis, or recommend actions. These 
sub-strategies, which provide global 
rhetorical coherence, can expand to a range of 
predicate types such as the three chosen in 
the example plan. As figure 4 illustrates, 
the explanation strategy is a representation of 
(OCAI002 
(AIO (VALUE (AUX (VALUE 
(DISPLAY (VALUE 
(AIRCRAFT (POSSIBLE 
(VALUE 
(STATUS 
(HISTORY (VALUE 
(AIRBASE (POSSIBLE 
(ORDNANCE (POSSIBLE 
(TARGET (VALUE 
(STATUS 
(ACNUMBER (POSSIBLE 
(VALUE 
(STATUS 
Figure 3. 
(OCA))) 
(OCA1002-AUX))) 
(#<MISSION-WINDOW I 1142344 dccxposcd>))) 
((F-4C F-4D F-4E F-4G F-111E F-lllF))) 
(F-111E)) 
(USER))) (<#EVENT INSERT TARGET BE30703 USER>))) 
((ALCONBURY)))) 
((A1 A2 ... A14)))) 
(BE30703)) 
(USER))) ((1 2 ... 25))) 
(3)) 
(USER)))) 
Mission Frame in FRL 
- 170 - 
EXPLAIN 
PROBLEM IDENTIFICATION SUPPORT RECOMMEND 
i ,/ \ 
conficting slots 
highlighted characteristic classificatory suggestive 
on screen 
Figure 4. Dominance (arrows) and Ordering (sequential equilevel nodes) relationships 
both dominance and ordering among the 
predicates as well as a means for powerful 
aggregation of predicates into substrategies. 
distinguishes between the two fighter entities 
indicating the deeper reason why the choice is 
recommended. This knowledge originates 
(CHARACTERISTIC 
((OCA1001)) 
((AIRCRAFT F-111E) (TARGET NIL NIL BE30703))) 
(CLASSIFICATORY 
((LUDWIGSLUSTS -ALPHA)) 
((ELECTRONICS NIL NIL NIL NIL NIL ((FUNCTION (EW-GCI)))))) 
(SUGGESTIVE 
((AIRCRAFT SELECTED)) 
((F-4G NIL NIL NIL NIL NIL ((FUNCTION (RADAR-DESTRUCTION)))) 
(F-111E NIL NIL NIL NIL NIL ((FUNCTION (RADAR-SUPPRESSION)))))) 
Figure 5. Selected Rhetorical Propositions. 
The corresponding instantiated rhetorical 
propositions are shown in figure 5. The 
problem to be identified in our illustration is 
that there is a conflict between the aircraft and 
the target chosen in the mission plan. As this 
is indicated by highlighting of these slots on 
the screen, identification of the conflict is not 
included in the text, although there is no 
reason why this could not be explicitly stated 
by means of a definition predicate. With the 
problem identified, the planner justifies this 
identification by characterizing the mission 
under consideration and classifying the object 
at the root of the constraint violation. 
Finally, the planner recommends a viable 
alternative using a suggestive proposition. 
Notice that the discriminatory knowledge 
in the suggestive predicate in figure 5 
from the knowledge base 1 rather than the 
explanation trace. Thus the knowledge 
provided in the audit trail along with general 
knowledge from the domain knowledge base 
are abstracted into rhetorical predicates which 
serve as sentential building blocks of text. 
Attachment points for linguistic units (parts- 
of-speech, phrases, or complete utterances) 
are indicated by position in the rhetorical 
formalism. Prepositional phrase selection is 
guided by keywords such as function (for), 
location (in, on, under), or instrument (with, 
using). 
1These distinguishing descriptive attributes, implicit 
in the expert system, were explicidy added to 
discriminate entities on the basis of structure, 
function, location, etc. 
- 171 - 
The rhetorical formalism is interpreted 
with a case-frame semantics which is 
translated to syntactic form via a relational 
grammar. Discourse models of focus and 
context as well as rhetorical force guide 
syntax choices. Morphological synthesizers 
(using category and feature values from the 
syntax generator) together with orthographic 
routines govern f'mal surface form (see figure 
6). As illustrated in the final sentence of the 
paragraph, parenthetical functional 
justifications enhance the explanation by 
providing additional information from the 
knowledge base which was relevant but not 
included in the original explanation. 
levels of representation in EXPLAN can be 
viewed from this perspective. 
Yet another area for further research 
concerns the replanning of explanations in 
reaction to user feedback (Moore and 
Swartout, 1988). Because of the explicit 
representation of rhetorical structure, models 
of discourse context (histories of foci, 
rhetoric, and content), and alternative 
explanation strategies, EXPLAN offers a rich 
basis for investigating recovery strategies 
from a variety of explanation error states. 
For example, input which indicates user 
misconception should guide the explanation 
Why did the mission plan fail? 
Offensive Counter Air Mission 1002 has f- 11 le aircraft and a target of Ludwigslusts-Alpha. 
Ludwigslusts-Alpha is electronic hardware for early warning and ground counter interception. 
Therefore, the aircraft should be an f-4g (for radar destruction) rather than an f-11 le (for radar 
suppression). 
Figure 6. Rhetorically organized explanation of rule conflict. 
DISCUSSION 
The produced text is more effective 
because of explicit rhetorical organization, the 
use of textual connectives (e.g. "therefore"), 
and the enrichment of the explanation with 
additional justificatory knowledge. An 
interesting venue for further investigation, the 
order and dominance relationships of figure 4 
could aid in responding to user 
misconceptions or follow-up questions. 
These relationships could be used to tailor 
rhetorical force to the type of user addressed, 
hence requiring explicit user models. An 
obvious weakness is the lack of goal-directed 
selection of rhetorical devices to achieve 
some targeted effect. In essence, pragmatic 
function is implicit in the rhetorical strategies 
such that effects on the hearer are achieved, 
although not explicitly planned for. A 
particularly enticing idea is that put forward 
by (Hovy, 1988) suggesting the need for 
both prescriptive, top-down planning of 
rhetorical goals, coupled with selectional 
restrictions at the surface level. Indeed, the 
planned rhetorical and constrained realization 
system to be more concrete, such as 
providing specific examples. Alternatively, 
feedback which indicates that the user 
expertly follows the line of reasoning may 
suggest that the explanation strategy should 
minimize details or provide more abstract 
reasoning. As a consequence, a flexible 
explanation generator must be able to select 
from multiple views of the underlying 
knowledge, such as structural versus 
functional representations (Suthers, 1988). In 
summary, the ability to provide justification 
dynamically using a range of explanation 
strategies will greatly enhance the perspicuity 
and utility of complex knowledge based 
systems. 
CONCLUSION 
The EXPLAN system demonstrates the 
effectiveness of rhetorical organization, 
textual connectives, and justificatory 
enhancement of explanation traces to achieve 
more cohesive text. A more effective 
- 172- 
explanation/generation system will use 
knowledge about the user to select rhetorical 
structure, content, and surface choices and 
will be flexible enough to handle a variety of 
follow-up questions. These are the foci of 
current research. 
ACKNOWLEDGMENTS 
I would like to thank Professor Karen 
Sparck Jones for many enlightening 
discussions on issues concerning explanation 
and natural language generation. 
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