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<Paper uid="W91-0114">
  <Title>SHARED PREFERENCES</Title>
  <Section position="6" start_page="116" end_page="117" type="concl">
    <SectionTitle>
5 Discussion
</SectionTitle>
    <Paragraph position="0"> Related Work: There is an enormous amount of work on preferences for understanding, e.g., \[Whittemore, Ferrara, and Brunner, 1990\], \[Jensen and Binot, 1988\], \[Grosz, Appelt, Martin, and Pereira, 1987\] for a few recent examples. In work on generation preferences (in the sense of rankings of structures) are less clearly identifiable since such rankings tend to be contained implicitly in strategies for the larger problem of deciding what to say (but see \[Mann and Moore, 1981\] and \[Reiter, 1990\].)i Algorithm 1 is similar in spirit to the &amp;quot;all possibilities plus constraints&amp;quot; strategy that is common in principle-based approaches (see \[Epstein, 1988\])i, but it differs from them in that it imposes a preference ordering on interpretations, rather than rest'ricting the set O f legal interpretations to begin With.</Paragraph>
    <Paragraph position="1"> I Strzalkowski \[Strzalkowski, 1990\] contrasts two strategies for r~versibility: those with a single grammar and two intepreters versus those with a single interpreter and two grammars. Although the top-level algorithm presented here works for both understanding and generation, the underlying generatio~ and understanding algorithms can belong to either of Strzalkowski's categories.</Paragraph>
    <Paragraph position="2"> However, the more specific algorithms discussed in Section 4 belong to the former category. There is also a clear &amp;quot;directionality&amp;quot; in both the scope and the anaphora Preferences; both are basically understanding h~euristics that have been reformulated to work b~i-directionally. For this reason, they are both considerably weaker as generation heuristics. In particular, the anaphora Preference is clearly insufficient as a method of choosing when to use a pronoun. At best, it can serve to validate the choices made by a more substantial planning compoOent.</Paragraph>
    <Paragraph position="3"> The Two Directions: In general, it is not clear J what the relation between understanding and generation heuristics should be. Formulae 4 and 5 are reasonable requirements, but they are too weak to provide ithe close linkage between understanding and generation that we would like to have in a bi-directional system. On the other hand, Formula 6 is probably too strong since it requires the equlivalence classes to be the same across the boardl In particular, it entails the converse of Formula.4, and this has counter-intuitive results. For example, consider any highly convoluted, but grammatical , sentence: it has a best interpretation, and by Formula 6 it is therefore one of the best ways of expressing that meaning.</Paragraph>
    <Paragraph position="4"> But if it is sufficently opaque, it is not a good way of saying anything. Similarly, a speaker may suddenly use a pronoun to refer to an object in a distant part of the discourse. If the anaphora Preference is sophisticated enough, it may resolve the pronoun correctly, but we would not want the generation system to conclude that it should use a pronoun in that situation. One way to tackle this problem is to observe that understanding systems tend to be too loose (they accept a lot of things that you don't want to generate), while generation systems are too strict (they cover only a sub-set of the language.) We can therefore view generation Preferences as restrictions of understanding Preferences. On this view, one may construct a generation Preference from one for understanding by adding extra clauses, with the result that its ordering is a refinement of that induced by the understanding Preference.</Paragraph>
    <Paragraph position="5"> Internal Structure: Further research is necessary into the internal structure of Preferences.</Paragraph>
    <Paragraph position="6"> We chose a very general definition of Preferences to start with, and found that further restrictions allowed for improvements in efficiency. Preferences that partition input into a fixed set of equivalence classes that can be determined in advance (e.g., the Preference for lexical choice discussed in Section 4) are particularly desireable since they allow structures to be categorized in isolation, without comparing them to other alternatives. Other Preferences, such as the scope heuristic, allow us to create the desired structures directly, again without need for comparison with other trees. On the other hand, the anaphora Preference is based on an algorithm that assigns rational-valued scores to candidate antecedents. Thus there can be arbitrarily many equivalence classes, and we can't determine which one a given candidate belongs to without looking at all higher-ranked candidates. This is not a problem during understanding, since the Proposer can provide those candidates efficiently, but the algorithm for generation is quite awkward, amounting to little more than &amp;quot;make a guess, then run understanding and see what happens.&amp;quot; The focus of our future research will be a formal analysis of various Preferences to determine the characteristic properties of good understanding and generation heuristics and to investigate methods other than Formula 9 of combining multiple Preferences. Given such an analysis, Algorithm 1 will be modified to handle multiple Preferences and to treat the different types of Preferences differently, thus reducing the need for  the kind of heuristic-specific algorithms seen in Section 4. We also plan an implementation of these Preferences as part of the KBNL system \[Barnett, Mani, Knight, and Rich, 1990\].</Paragraph>
  </Section>
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