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<Paper uid="W98-1403">
  <Title>A PRINCIPLED REPRESENTATION OF ATTRIBUTIVE DESCRIPTIONS FOR GENERATING INTEGRATED TEXT AND INFORMATION GRAPHICS PRESENTATIONS</Title>
  <Section position="4" start_page="18" end_page="19" type="metho">
    <SectionTitle>
2 Referential-Attributive Distinction in Language
</SectionTitle>
    <Paragraph position="0"> \[Donnellan1977\] describes two different possible uses of definite descriptions: 1 An attributive description's main function is to convey information directly contributing to the communicative goals of a discourse, whereas a referential description's only function is to enable the audience to identify a particular referent. This is a useful distinction for dialogue systems. In the case of failure of a referential description, a system might try to identify the referent again by giving:an alternate description, as illustrated in (1) below. However, when a description is used attributively, the content of the description plays a different role. In (2a), the required textbook for CS500 is used attributively to indirectly inform the user of how she might assess the difficulty of CS500 herself; the content of the description contributes to the user's recognition of the system's reason for suggesting that she read the book. In contrast in (2b), where an alternate description is used, the user is unable to recognize the systems's intention. In contrast to (1), (2c) illustrates that when all attributive description fails, a different type of followup by the system is required, one that explicates its intention. Also, as Donnellan points out and as can be seen by comparing (!) and (2c), the same description (the required textbook for CS500) can be used either referentially or attributively on different occasions depending on the speaker's intentions.</Paragraph>
    <Paragraph position="1">  (!) User: I want to read an introductory AI book. What do you recommend? System: The required textbook for CS500.</Paragraph>
    <Paragraph position="2"> User: Which book is that? System: Artificial Intelligence, by Richardson.</Paragraph>
    <Paragraph position="3"> User: Oh:.</Paragraph>
    <Paragraph position="4"> (2a) User: How difficult is CS5007 System: Read the required textbook for CS500.</Paragraph>
    <Paragraph position="5"> (2b) User: * How difficult is CS5007 Syste,n: Read Artificial Intelligence, by Richardson.</Paragraph>
    <Paragraph position="6"> User: Why? System: It is therequired textbook for CS500.</Paragraph>
    <Paragraph position="7"> (2c) User: How difficult is CS500?  System: Read the required textbook for it.</Paragraph>
    <Paragraph position="8"> User: VV'hy? System: If you find the textbook difficult, then you'll find the course difficult. In addition to its importance in determining appropriate dialogue fo!lowup behavior, the referential-attributive *distinction is important for generating effective text. As was *shown in (2a), the content of an attributive description may contribute directly to achieving communicative goals. To give another example, suppose that a user, who wants to buy a house in Somerset County, has asked for information about realtors serving Somerset County. 2 The overall goal of the system is for the User to believe that it may be beneficial to do business with a certain real estate agency, Realtors Inc. In that case, the system might generate (3), where (3)ii is intended to provide motivation for (3)i. That is, the description the city with the largest population in Somerset County was selected bythe system for its motivational value. In a system that does not distinguish referential from attributive (i.e., treats all uses of descriptions as referential), there is nothing preventing it from generating (4) or (5) instead, assuming that the city with the largest population in Somerset County, Berlin,. and the city with the worst pollution in Somerset County are three descriptions of the same object (which we refer to below by the internal system identifier $BERLIN).</Paragraph>
    <Paragraph position="9"> i Although DonneUan did not address uses of indefirdte descriptions, following \[Kronfeld1990\] we apply Dorme!Ian's distinction to them as Well. Also, to be precise, we are interested in what \[Kronfeld1986, Kronfeld1990\] terms :the modal aspect of Donnellan's distinction.</Paragraph>
    <Paragraph position="10"> :The information in this'and all other examples in the paper is fictitious.</Paragraph>
    <Paragraph position="11">  (3)i. We recommend Realtors Inc.</Paragraph>
    <Paragraph position="12"> ii. Realtors Inc. serves the city with the largest population in Somerset County. (4)i. We recommend Realtors Inc.</Paragraph>
    <Paragraph position="13"> ii. Realtors Inc. serves Berlin.</Paragraph>
    <Paragraph position="14"> (5)i. We recommend Realtors Inc.</Paragraph>
    <Paragraph position="15"> ii. Realtors Inc. serves the City with the worst pollution in Somerset County.</Paragraph>
    <Paragraph position="17"> However, (4)ii is not as effective as (3)ii if the user doesn't know or have in mind that Berlin has the largest population. Even worse, (5)ii might have an effect opposite to the one intended.</Paragraph>
    <Paragraph position="18"> A possible solution might be for the system to include as an additional proposition to be asserted with (4), the proposition that tBERLIN is the city with the largest population in Somerset County, yielding (6). On the other hand, there is nothing in the supposed underlyling representation of (6) to prevent (7) from beinggenerated, which may have a less than desirable effect, * (6)i, We recoinmend Realtors inc ....</Paragraph>
    <Paragraph position="19"> ii. Realtors Inc. serves Berlin. : iii. Berlin has the largest population in Somerset County.</Paragraph>
    <Paragraph position="20"> (7)i. We recommend Realtors Inc.</Paragraph>
    <Paragraph position="21"> ii. *Realtors Inc. serves the city with the worst pollution in Somerset.County.</Paragraph>
    <Paragraph position="22"> iii. That. city has the largest population in Somerset County.</Paragraph>
  </Section>
  <Section position="5" start_page="19" end_page="20" type="metho">
    <SectionTitle>
3 The Role of Attributive Descriptions in Task-Based Graphic
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="19" end_page="20" type="sub_section">
      <SectionTitle>
Design '
</SectionTitle>
      <Paragraph position="0"> As this section will illustrate shortly, different graphic designs may enhance or detract from a user's performance of certain types of perceptual and cognitive tasks. The philosophy of task-based graphic design is to design an information graphic based upon which perceptual and cognitive tasks the user wants or needs to perform. In our architecture (described more fully in the next section)~ the graphics generator reasons about what user tasks would enable the system's presentation'goals to be achieved, so that graphics can be designed to support those tasks (and thus support the presentation goals). Note that since the descriptions in Our domain of application are often fairly * complex (e.g., 90~o of the total weight of the cargo arriving by day 25), we assume that a compositi0nalappr0ach to representing attributive descriptions will facilitate the automatic transformation of presentation goals to user tasks.</Paragraph>
      <Paragraph position="1"> To see how different graphic designs about the same data may facilitate different tasks, consider Figure 1. In (a), the table shows that Arlington's population is .5K, Berlin's is 1K, etc. Moreover, * it is possible to compute from the data shown in it that Arlington's population is half that of Berlin's, that Berlin has the largest population, and that Berlin's population is greater than the population of all of the other towns combined. To facilitate just task (A), the task of looking up the population of a town given its name, then this table would be adequate. On the other hand, a bar chart such as the one Shown in (b) would better support both tasks (h) and (B), where (B) is the task 0fdetermining the largest and the smallest town. (Each vertical bar represents a particular town and ~the height of a bar represents the population of the town represented by the bar.) Ordering the towns by population size, as in (c), further facilitates task (B), as can be seen by comparing (b) to (c). However, task (C), the task of comparing Berlin's population to the total population of all of the other towns, would be facilitated by the chart shown in (d). In it, task (C) is facilitated by enabling the user to count the divisions of each bar. Also, if task (A) is not required, it is not necessary t O provide numeric values on the horizontal axis in (d).</Paragraph>
      <Paragraph position="3"> Si~lce in our approach the graphics generator reasons about what user tasks would enable the system's presentation goals to be achieved, it is important for the system to distinguisli cases where the content of a description itself directly contributes to the presentation's goals, i.e., where the content has an attributive rather than a referential function. For instance, suppose that a system must design a graphic supporting *the presentation goals described for example (3) above. These goals could* be achieved by the user's successful performance of task (B)above, and additionally, task (D), the task of looking up the real estate agency for that town. These tasks would be facilitated by a graphic such as (e)in Figure i, which facilitates both tasks. In contrast, if the system provided only table (f) of Figure 1, task (D) but not task (B) would be facilitated, and.thus the overall presentation might not be as effective.</Paragraph>
    </Section>
  </Section>
  <Section position="6" start_page="20" end_page="21" type="metho">
    <SectionTitle>
4 Overview of Generation Architecture
</SectionTitle>
    <Paragraph position="0"> As reported in a previous paper \[Kerpedjiev et a1.1997\], we are investigating the integration of two complementary approaches to automatic generation of presentations: hierarchical planning to achieve communicative goals and task-based graphic design. Many researchers in natural language processing, e.g., \[Moore1995\], have modeled presentation design as a process of hierarchical planning to achieve communicative goal s. Researchers in graphics have emphasized the need to design presentations that support the perceptual and logical tasks a user must perform \[Beshers and Feiner1993, Casneri991, Roth and Mattis1990\]. In our hybrid approach, a hierarchb cal planner \[Young1994\] is used to refine genre-specific but media-independent presentation* goals into genre-independent and media-independent subgoals. (For Simplicity, in the rest of this paper we shall refer to the genre-independent and media-independent level of the plan just as the media-independent level.) These media-independent goals are achieved by media-independent il-Iocutionary actions \[Searle1970\], e.g., Assert, and Recommend, which themselves are decomposed into media-independent actions that correspond to attributive and referential uses of descriptions.</Paragraph>
    <Paragraph position="1"> (Tile language used in our current system to express the content.of illocutionary acts and goals is described in \[Green et a1.1998\]. In addition to application-specific terms, the language includes  more broadly applicable terms for expressing quantitative relations and aggregate properties.) The media-independent plan is used by two media-specific generators (one for text, another for graphics) to create parts of the presentation. (The problems of media-allocation, how the system decides what parts of the presentation to realize in which media, and media-coordination, how it coordinates information conveyed in both media, are beyond the scope of this paper.) The text generator converts parts of the plan (as determined by the media-allocatiom component) to funct!onal descriptions (FDs) of sentential units, which specify, for example, semantic predicate-argument structure, open-class lexical items, and aspects of sentence structure with pragmatic import. The FDs are subsequently realized by a general-purpose sentence genera.tor (FUF/SURGE) \[Elhadad and Robin1996\]. (Decisions regarding the content of referential descriptions and anaphora, which are made by the text generator, are beyond the scope of this paper.) The first stage of thegraphics generator converts parts of the plan (as determined by the media-allocation component)to a sequence of logical user tasks that will enable the presentation's goals to be achieved; the task sequence is then inPut to the SAGE graphic design system \[Roth and Mattis1990, Chuah et a1.1995, Roth et a1.1994\], which automatically creates a graphic supporting the user's tasks. 3 For example, the presentation goal that the user know the population of Arlington would be enabled if the user were able to perform the sequence of logical tasks of searching for Arlington in a graphic, finding its population attribute, and then looking up the value; furthermore, these tasks could be performed using a graphic such as (a) in Figure 1. (The process of converting acts of the plan to tasks is partly described in \[Kerpedjiev et a1.1998\] and is * beyond the scope of this paper.)</Paragraph>
  </Section>
  <Section position="7" start_page="21" end_page="24" type="metho">
    <SectionTitle>
5 Planning Attributive Descriptions
</SectionTitle>
    <Paragraph position="0"> This section describes * how the two types of actions corresponding to attributive and referential uses of descriptions are created and represented in the media-independent planning phase of generation in our system. Our system uses media-independent presentation operators to perform content* selection and high~level *organization of the presentation. For example, Figure 2. shows a simplified version of the presentation operator that would be used to generate (3) above, in the formalism used by the presentation planner \[Young1994\]. The strategy encoded in this decomposition is to recom* mend anaction, as in (3)i, and to provide information that may motivate the audience* to adopt the recommendation, as in (3)ii. 4 The plan parameter ?p2 would be instantiated-with the proposition describing the recommended action. 5 The Motivate plan constraint of the operator would instantiate the plan variable ?pl with the proposition expressed in (3)ii. In our current system, the Search for a proposition satisfying a constraint such as the Motivate constraint in the example is *performed by accessing a database created by a domain-specific data analysis component. For * example, in our current application domain the data analysis component analyzes transportation schedules and records features that may be of interest to the user.</Paragraph>
    <Paragraph position="1"> Propositions such as ?p2 and ?pl are represented in a RQFOL (first-order logic with restricted quantification). RQFOL has been used for representing the meaning of natural language queries  RQFOL distinguishes information about discourse referents from the main predication of an expression. For example, the Proposition plan constraint of the operator in Figure 2, makes use of the RQFOL representation of ?pl to extract information with which to instantiate the plan variables ?main-predl and ?refsl with the main predication of ?pl and a list describing the discourse entities \[Webber1983\] evoked or accessed by use of ?main-predl, respectively. (The significance to presentation generation of the distinction between the main predication and information about discourse referents is discussed ill \[Green et M.1998\].) The step of the operator shown in Figure 2 underlying (3)ii is an Assert action. In general, Assert(?prop, ?refs) is defined as the System asserts ?prop to the User, where ?refs is a list specifying all discourse entities evoked or accessed by use of ?lnvp. 6 Discourse entities are specified in the list either by an internal identifier (an identifier referring to a database object) or by descriptions stated as RQFOL expressions.</Paragraph>
    <Paragraph position="2"> For example, consider the Assert action underlying (3)ii~ which can be represented as follows: 7</Paragraph>
    <Paragraph position="4"> The variable ?prop has been instantiated with serves($RI, d2), where SRIand d2 are discourse entities; the variable ?refs is instantiated with a list specifying six discourse entities:  * Figure 3 shows the definition of an abstract Assert action and a simplified version of its decomposition. An Assert may be decomposed into three types of subactions. Predicate is used to describe an event independently of th e things that play a role in that event.Activate-ko is a primitive action used to refer to an object,*i.e., this corresponds to the referential use of a description. To achieve the effect of this action, the text and graphics generators are free to select an3, device that will enable the user to identify the object (subject to pragmatically appropriate identification constraints \[Appelt and Kronfeld1987\]). In other words, since the function of the description is purely referential, its content does not contribute directly to the presentation's goals and thus is not represented in the plan *. Activate-as is used to refer to a discourse entity as the object fitting the description provided, i.e., tliiS corresponds to the attributive use of a description. An Activate-as may itself be decomposed into these three types of subactions.</Paragraph>
    <Paragraph position="5"> During hierachical planning, the constraints of the Assert decomposition operator (shown in Figure 3) are used to instantiate the plan variables lid-list and ?desc.list. In the forall step of the operator, :an Aetivate-ko and Activate-as action is created for each element of ?id-listand ?desclist, respectively. E.g., for the Assert shown above representing (3)ii, the ?id'list would contain the identifiers SRI and $SOMERSET, and ?desc-list would include the descriptions of d2 through d5.</Paragraph>
    <Paragraph position="6"> Then, the Assert shown above would be partly decomposed into attributive and referential communicative actions as follows: SRIis the object of an Activate-ko act, and d2 is decomposed into an Activate-as act describing d2, which in turn is decomposed into an Activate-as describing d3, and so on, ending with an Activate-ko to enable the audience to identify $SOMERSET. In general, a complex attributive description may contain one or more Activate-ko acts. That is, our representation scheme supports the composition of descriptions for attributive use from subcomponents whose use may be attributive or referential. Thus, in this example, $SOMERSET could be described in a number of ways, e.g., Somerset County or the county on the eastern side of Westmoreland County.</Paragraph>
    <Paragraph position="7"> To summarize the process of generating attributive descriptions in our approach, discourse strategies such as Recommend-act (shown in Figure 2) determine content selection as well as whether the selected information will be presented as part of the main predication or as part of all attributive description. The ill0cutionary act operators (e.g., Assert) and Activate-as operator further. decompose any descriptions into Activate-as and Activate-ko acts. *Thus, the *system's intentions are represented in the presentation plan, enabling appropriate text and graphics to be generated.</Paragraph>
    <Paragraph position="8"> For *example, because the information associated with d2 (the city with the largest population in Somerset county) is part of the above plan, the graphic generator will attempt to produce a graphic such as (e) in Figure 1 that will enable the user to see that the agency serving the town with the largest population is *Realtors Inc. Without such a specification in the plan, a graphic might be  designed showing only that Realtors Inc. serves Berlin, or worse., that Realtors Inc. serves the city with the worst pollution in Somerset County. (For examples of how different Communicative intentions can be distinguished in graphics see \[Green et a1.1998\].)</Paragraph>
  </Section>
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