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<Paper uid="C88-1037">
  <Title>Expressing quantifier scope in French generation</Title>
  <Section position="2" start_page="0" end_page="182" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Natural language interfaces are being applied to various applications; most of them, especially data base and expert system interfaces, require the production of precise texts. One of the key problems of precision is the correct expression of quantifier type and scope.</Paragraph>
    <Paragraph position="1"> Our research has led us to identify a new practical method to express quantification in a precise and natural way and to implement our approach in the portable written French generator called Hermbs (/Gailly 87a/and/Gailly 875/) to be integrated in a portable natural language interface (/Binot, Gailly &amp; Ribbens 86/). The generator's goal is to produce French sentences from a message (a representation of their meaning) expressed in Formula, a representation language based on an extension of first order predicate calculus. The main point of our approach is the identification of the importance of properly choosing- the determiners in noun phrases in conjunction with the selection of the surface structure of the sentences produced. However, we must stress the fact that we are not trying to provide a theoretical and comprehensive study of how quantification is expressed in natural language (such as in the work of/Cooper 83/).</Paragraph>
    <Paragraph position="2"> In the rest of this paper we will first discuss the difference between the problems raised by quantification in parsing and generation, then discuss the present approaches to the problem and finally present our own approach.</Paragraph>
    <Paragraph position="3"> 2 The difference between understanding and generating quantifier scope In understanding, the problem is, starting from the input sentence, to identify the quantifier scope intended by the user. In presence of an ambiguous sentence, the system will use all available information (i.e. syntactic structure, word choice and order, semantic and pragmatic knowledge, context and dialog history) to find the most plausible quantifier order. Consider: Ex. 1 Un homme meurt tou~ los soirs.</Paragraph>
    <Paragraph position="4"> (A man dies all evenings)) Ex. 2 Chaquc s:oir un homme meurt.</Paragraph>
    <Paragraph position="5"> (Every evening a man dies.) Although the first example is poorly expressed, a clever understanding system should comprehend both sentences in the same way, as human beings do, because the interpretation &amp;quot;The same man dies every evening.&amp;quot; is nonsensical. If the verb &amp;quot;mcurt&amp;quot; (&amp;quot;dies&amp;quot;) is replaced by &amp;quot;mange chcz Paul&amp;quot; (&amp;quot;eats at Paul's place'), the two sentences have different meanings and the system should understand them in different ways.</Paragraph>
    <Paragraph position="6"> *Research sponsored by the Institut pour l'Encouragement de la Recherche Scientifique dens PIndustrie et l'Agricultnre (LR.S.I.A.). 1Though we have trie&amp;.to provide a faithful translation of the examples, we have kept the French sentence structure in the English version; therefore the translation can sometimes prove poor.</Paragraph>
    <Paragraph position="7">  In generation, the goal is to express the quantifier scope imposed by the input message correctly and as clearly as possible in order to avoid ambiguity.</Paragraph>
    <Paragraph position="8"> We should not rely on the user's domain knowledge but provide him with clear clues about quantifier scope. Therefore, we should never produce unclear sentences such as example 1.</Paragraph>
    <Paragraph position="9"> 3 Existing approaches to the problem Few authors considered the problem of expressing quantifier scope correctly and in a natural way. Surveys of the field (e.g. /Mann 82/or/McKeown 86/) or even workshops proceedings (e.g. /Appelt 85/) do not mention the problem. At present, the problem seems to be solved in two ways: * By indenting the text produced, (especially in database query reformulation), in order to show the structure of the text (scope of quantifiers and propositional connectives) graphically. Though this method can prove useful in small scale applications (e.g./De Roeck 8z Lowden 86/and some commercial systems), in fact it avoids the problem.</Paragraph>
    <Paragraph position="10"> * By using word order 2 as suggested by /Chester 76/ and /Grishman 86/. The surface structure is chosen in such a way that the variable bound by the most dominating quantifier is generated as tile leftmost noun phrase and so on. The &amp;quot;word order&amp;quot; method is not satisfactory. Though word order is kept unchanged in the following examples, different scopes are clearly indicated by choosing adequate determiners: Ex. 3 Chaque chien poursuit un chat.</Paragraph>
    <Paragraph position="11"> (Each dog is chasing&amp;quot; a cat,) Ex. 4 Tousles chiens poursuiven~ un m~mc cha~.</Paragraph>
    <Paragraph position="12"> (A11 dogs are chasing a same cat.) As far as French is concerned, /Colmerauer 77/ proposed a more extensive treatment of the subject for parsing. His approach is based on the syntactic function of noun phrases and word order. Again, this method is not satisfactory: determiner selection allows to express different scopes, keeping word order :and syntactic functions unchanged: Ex. 5 J'ai rcncontrd l'armatcur de chaquc navirc.</Paragraph>
    <Paragraph position="13"> (I met the owner of each ship.) Ex. 6 J'ai rcncontrd l'armatcur de tousles navires.</Paragraph>
    <Paragraph position="14"> (I met the owner of all ships.)</Paragraph>
  </Section>
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