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<Paper uid="E89-1023">
  <Title>ENHANCING EXPLANATION COHERENCE WITH RHETORICAL STRATEGIES</Title>
  <Section position="4" start_page="0" end_page="0" type="metho">
    <SectionTitle>
KNOWLEDGE REPRESENTATION
and EXPLANATION
</SectionTitle>
    <Paragraph position="0"> 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).</Paragraph>
    <Paragraph position="1"> 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.</Paragraph>
    <Paragraph position="2"> - 168 The choice for AIRCRAFT is in question because: BY TARGET-AIRCRAFr-1:1 THERE IS A SEVERE CONFLICT BETWEEN</Paragraph>
  </Section>
  <Section position="5" start_page="0" end_page="0" type="metho">
    <SectionTitle>
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
</SectionTitle>
    <Paragraph position="0"> 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.</Paragraph>
    <Paragraph position="1"> Moreover, it lacks essential content that specifies why an F-111E is not acceptable.</Paragraph>
    <Paragraph position="2"> That &amp;quot;F- 111E IS NOT A F-4G&amp;quot; makes little contribution to the justification, and at best implicitly suggests an alternative (an F-4G).</Paragraph>
    <Paragraph position="3"> 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).</Paragraph>
    <Paragraph position="4"> The explanation trace is of the form:</Paragraph>
    <Paragraph position="6"> where * means 1 to N repetitions. In the example, the rule constraint is TARGET- null 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  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 -</Paragraph>
  </Section>
  <Section position="6" start_page="0" end_page="0" type="metho">
    <SectionTitle>
EXPLANATION RHETORIC
</SectionTitle>
    <Paragraph position="0"> 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.</Paragraph>
    <Paragraph position="1"> 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).</Paragraph>
    <Paragraph position="2"> 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).</Paragraph>
    <Paragraph position="3"> These predicates are grouped into subschema 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  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  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.</Paragraph>
    <Paragraph position="4"> Finally, the planner recommends a viable alternative using a suggestive proposition.</Paragraph>
    <Paragraph position="5"> 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.</Paragraph>
    <Paragraph position="6"> Attachment points for linguistic units (partsof-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).</Paragraph>
    <Paragraph position="7"> 1These distinguishing descriptive attributes, implicit in the expert system, were explicidy added to discriminate entities on the basis of structure, function, location, etc.</Paragraph>
    <Paragraph position="8"> - 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.</Paragraph>
    <Paragraph position="9"> levels of representation in EXPLAN can be viewed from this perspective.</Paragraph>
    <Paragraph position="10"> 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.</Paragraph>
    <Paragraph position="11"> 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).</Paragraph>
    <Paragraph position="12"> Figure 6. Rhetorically organized explanation of rule conflict.</Paragraph>
  </Section>
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