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<Paper uid="W06-1413">
  <Title>The Clarity-Brevity Trade-off in Generating Referring Expressions [?]</Title>
  <Section position="3" start_page="0" end_page="89" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Until recently, GRE algorithms have focussed on the generation of distinguishing descriptions that are either as short as possible (e.g. (Dale, 1992; Gardent, 2002)) or almost as short as possible (e.g.</Paragraph>
    <Paragraph position="1"> (Dale and Reiter, 1995)). Since reductions in ambiguity are achieved by increases in length, there is a tension between these factors, and algorithms usually resolve this in some fixed way. However, the need for a distinguishing description is usually assumed, and typically built in to GRE algorithms.</Paragraph>
    <Paragraph position="2"> We will suggest a way to make explicit this balance between clarity (i.e. lack of ambiguity) and brevity, and we indicate some phenomena which we believe may be illuminated by this approach.</Paragraph>
    <Paragraph position="3"> The ideas in this paper can be seen as a loosening of some of the many simplifying assumptions often made in GRE work.</Paragraph>
    <Paragraph position="4"> [?]This work is supported by a University of Aberdeen  Sixth Century Studentship, and the TUNA project (EPSRC, UK) under grant number GR/S13330/01. We thank Ielka van der Sluis and Albert Gatt for valuable comments.</Paragraph>
    <Paragraph position="5"> 2 Clarity, Brevity and Cost  We consider only simple GRE, where the aim is to construct a conjunction of unary properties which distinguish a single target object from a set of potential distractors. Our notation is as follows. A domain consists of a set D of objects, and a set P of properties applicable to objects in D. A description is a subset of P. The denotation of S, written [[ S ]], is {x [?] D  |[?]p [?] S : p(x)}.</Paragraph>
    <Paragraph position="6"> (Krahmer et al., 2003) describe an approach to GRE in which a cost function guides search for a suitable description, and show that some existing GRE algorithms fit into this framework. However, they follow the practice of concentrating solely on distinguishing descriptions, treating cost as a matter of brevity. We suggest that decomposing cost into two components, for the clarity and brevity of descriptions, permits the examination of tradeoffs. Fornow, wewilltakethecostofadescription S to be the sum of two terms:</Paragraph>
    <Paragraph position="8"> where fC counts ambiguity (lack of clarity) and fB counts size (lack of brevity). Even with this decomposition of cost, some existing algorithms can still be seen as cost-minimisation. For example, the cost functions:</Paragraph>
    <Paragraph position="10"> allow the Full Brevity algorithm (Dale, 1992) to be viewed as minimising cost(S), and the incremental algorithm (Dale and Reiter, 1995) as hill-climbing (strictly, hill-descending), guided by the property-ordering which that algorithm requires. Whereas Krahmer et al.'s cost functions are (brevity-based) heuristic guidance functions, our alternative here is a global quantity for optimisation. Hence their simulation of Full Brevity  relies on the details of their algorithm (rather than cost) to ensure clarity, while our own cost function ensures both brevity and clarity.</Paragraph>
  </Section>
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