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<Paper uid="C88-1039">
  <Title>Semantic Interpretation of Pragmatic Clues: Connectives, Modal Verbs, and Indirect Speech Acts</Title>
  <Section position="2" start_page="191" end_page="191" type="metho">
    <SectionTitle>
(WANT USER (KNOW USER (I~,ECOMMI,IND SYSTEM P~)
</SectionTitle>
    <Paragraph position="0"> The interpretation of modal verbs is further infiuet~ood by eonnectiw~;; which may occur in complements. Consider the following sentence: (6) Meine Sehwester mug viel Geld habcn.</Paragraph>
    <Paragraph position="1"> 'My sister nmst have a lot of money.' In this case one can only infer that the speaker bo!ieves that the proposition is true, namely that his sister has a lot of money. The interpretation completely changes when we have: (7) Meine Schwester mug viel Geld haben, um th~ Haus zu bauen.</Paragraph>
    <Paragraph position="2"> ':My sister needs to have a lot of money in order to bnild her house.' It is possible that the speaker believes as in (6) that his sister has s lot of money, but this cannot be inferred from the statement. Here we can only infer that the speaker believes that the second proposition (his sister's building her house) implies the first one (his sister's having a lot of money).</Paragraph>
    <Paragraph position="3"> Connectivos Connective~ are a means of expressing the argumentative and logical structure of the speaker's opinions by linking' propositions. Such relations between propositions are classified into several categories such as inferential, temporal, causal linkages \[Cohen 84 and Br6e/Smit 86\]. The system interprets underlying beliefs and wants and enters them into the user nmdel in accordance with the different classes of connectives.</Paragraph>
    <Paragraph position="4"> As an example, take the class of connectives which express inferences of the speaker, e.g., (8) Ich will eine Anlage mit kurzer Laufzeit, damit ich schnell an mein Geld herankommen kann.</Paragraph>
    <Paragraph position="5"> 'i want a short term investment so that I can get my money back quickly.' Because of the connective damit the system concludes that the proposition of the second part of the sentence is the superordinate goal rather than the first proposition although this is the want which is expressed directly. The user supposes that the first proposition is a necessary condition for the second, which expresses his goal. When further processing this logical structure, the system can recognize the underlying misconception, namely that it is not the term of an investment which is important for getting the money back quickly, but the liquidity.</Paragraph>
    <Paragraph position="6"> The interpretation of connectives depends on the occur~ rence of modal verbs, as the following examples demonstrate: null  'Do I have to pay a fee to desolve my savings account? q In (9) the modal verb sollen inside the question indicates that the user wants a recommendation. It indicates further that the connective um-zu has to be interpreted as a user's want. The correct interpretation is that the user wants to know whether the system would recommend that the user attempts to attain a certain goal (paying off his mortgage) by selling his securities.</Paragraph>
    <Paragraph position="7"> Such a want is not inferrable from (10). It may be that the user wants to desotve his savings account at somc time in the future, but the modal verb mtissen (must) inside the question does not indicate a current want. Therefore only the relation between the two propositions is the focus of attention. Hence we can paraphrase the user's want as 'Do I have to pay a fee if I want to desolve my savings account?', or, again more formally,</Paragraph>
  </Section>
  <Section position="3" start_page="191" end_page="193" type="metho">
    <SectionTitle>
(WANT USER (KNOW USER (IMPLIES P2 PLY)),
</SectionTitle>
    <Paragraph position="0"> where P2 denotes the desolving event and P1 the fee paying.</Paragraph>
    <Paragraph position="1"> The Computational Model The processes described in this paper work on a formal representation of utterances which reflects their semantic structure but also contains lexical and syntactic information (hedges, connectives, modal verbs, tense, and mood) which has not yet been interpreted. Our formal representation language is called IRS (Interne ReprtisentationsSprache, \[Bergmann et. al. 87\]). It contains all the standard operators of predicate calculus, formalisms for expressing propositional attitudes, modalities, and speech acts, natural language connectives (and. or, however, therefore, etc.), a rich collection of natura/!anguage quantifiers (e.g., articles, wh-particles), and modal operators (maybe, necessarily).</Paragraph>
    <Paragraph position="2">  supposed to have a term of  Fig. 3 shows a part of the syntax definition of IRS and the representation of the sentence (6) Die Wertpapiere sollen eine Laufzeit yon vier Jahren haben 'The securities should/are supposed to have a term of four years.' This example contains some important features of IRS: Only one- and two-place predicates are allowed. They correspond to the concepts and roles defined in our terminological knowledge base QUIRK \[Bergmann/ Gerlach 86\] except for SOLLEN and HAS-TENSE which still need to be semantically interpreted.</Paragraph>
    <Paragraph position="3"> Quantifications are always restricted to a range which may be described by an arbitrary formula.</Paragraph>
    <Paragraph position="4"> The operator PROP allows for associating a variable to a formula. In subsequent terms the variable may be used as a denotation of the proposition expressed by that formula.</Paragraph>
    <Paragraph position="5"> In the formula given in Fig. 3 the variable A1 denotes the assertion as an action with agent USER and propositional content P1. $1 reflects the occurrence of the modal verb sollen which is represented like a predicate, but has not yet been semantically interpreted. The &amp;quot;propositional content&amp;quot; of S1 is P2 which denotes the proposition the securities have a term offouryears.</Paragraph>
    <Paragraph position="6"> For characterizing sets of structures to which one specific interpretation may apply, we use IRS patterns\[Gerlach 87\], i.e., highly parameterized semantic structures which specify an arbitrary combination of features relevant to the interpretation process: The surface speech act, tense information, modal hedges, and restrictions on the propositional content.</Paragraph>
    <Paragraph position="7"> 194, A quite simple example for an IRS pattern is given in Fig. 4. Its elements are variables (symbols starting with '?'), constants (all other symbols), a concept pattern (matching any one-place predication), role patterns (matching two-place predications).</Paragraph>
  </Section>
  <Section position="4" start_page="193" end_page="193" type="metho">
    <SectionTitle>
(AND (?INFO-TRANS-TYPE ?INFO-TRANS)
(HAT-SOURCE ?INFO-TRANS USER)
(HAT-GOAL ?INFO-TRANS SYS)
(HAT-OBJ ECT ?INFO-TRANS ?OBJ ECT))
</SectionTitle>
    <Paragraph position="0"> This pattern is used for matching the top level of the representation of an utterance of the user, directed to the system. When matching the variable ?OBJECT is bound to the whole propositional content of the utterance and is used by the subsequent steps of analysis.</Paragraph>
    <Paragraph position="1"> As described above, we do not only infer new user model information directly, but also perform transformations on IRS structures, e.g., to reduce idioms to more primitive speech acts. This kind oPS processing involves applying a set oftransformatlonal rules to an IRS formula where a rule is a pair of IRS patterns as described above (for an example, see Fig. 2). When instantiating the right hand side of the rule the interpreter will create new variables for unbound pattern variables and quantify them in the appropriate way (in Fig. 2 this is the case with the pattern variable ?Q).</Paragraph>
    <Paragraph position="2"> In WISBER the user model is a section of the central assertional knowledge base (A-Box, \[Poesio 88\]) which allows for storing and retrieving assertional knowledge in different contexts which denote the content of propositional attitudes of agents. Hence a new entry is added to the user model by storing the propositional content in the A-Box context which contains the user's wants.</Paragraph>
    <Paragraph position="3"> Conclusior~ We have implemented our interpretation module in an Interlisp programming environment. It is a part of the natural lahguage consultation '~ystem WISBER. The module's coverage includes all German modal verbs occuring in assections and questions, some connectives (e.g., * and, so that, because) and the most common indirect questions. On the one hand our future work will concentrate on extending the performance of the system inside the framework which is described in this paper. On the other hand we will integrate the concept of expectations, i.e. expectations the system has according to the users next utterance depending on the actual state of the dialog. Thi~ will enable us to resolve more kinds of ambiguities in user utterances.</Paragraph>
  </Section>
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