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<Paper uid="C88-2150">
  <Title>Generation as Structure Driven Derivation</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Using the same grammar formalism, or even the same grammar, for both analysis and synthesis is usually regarded as an elegant, efficient and sometimes even as the psychologically most plausible approach to natural language parsing and generation. In this paper we want to show that this approach can be realized within the LFG-framework by defining two generation algorithms. 1 The first permits the construction of generators for LFGs which generate sentences from fimetional structures. The second constructs generators which generate sentences from semantic structures.</Paragraph>
    <Paragraph position="1"> Both algorithms are based on concepts of derivation for LFGs which can he strengthened in such a way that the derivation can be driven by a given input structure. The principles which underlie the control mechanisms for the derivation arc sufilciently general to also be applicable to other unification based formalisms which allow the derivation of functional and/or semantic structures in parallel to constituent structures (e.g. PATI~ (of. e.g.</Paragraph>
    <Paragraph position="2"> \[Shieber 831, \[Karttunen 861) ).</Paragraph>
    <Paragraph position="3"> For the generation from semantic structures, a derivation concept of this type can be defined, if, following a proposal by Halvorsen and Kaplan (cf. \[Halvorseu 87\], \[Kaplan 87\]), projector equations are used to describe (co-describe) the semantic structure of a sentence. Since the projector mechanism is independent of the specific type of semantic theory, the algorithm works as long as this mechanism is used to build up semantic representations. null In addition, the derivation driven by the semantic structure can be directed by constraints over the f-structure. Usually, semantic information by itself is regarded as providing a basis for generation which is too weak to capture relevant distinctions in the surface form. This additional mechanism could contribute to make the generation more sensitive to syntactic and pragmatic information. Thus, additional fimctional information can enforce a specific syntactic realization, such as passivization, topicalization, extraposition or discontinuous realization of constituents.</Paragraph>
    <Paragraph position="4"> It should be clear that the specific constraints which have to be or can be imposed on this kind of generation are subject to empirical studies on questions of syntax, discourse and dialogue. 2 Similarly, for machine translation, one will need interlingual comparative research on these subjects to establish what relevant functional information can be drawn directly from the f-structures of the source text, in order to guarantee a coherent target text. These important empirical questions will not be addressed directly in this paper. We consider it an essential requirement for an adequate generation algorithm, however, that it provides the respective possibilities of control.</Paragraph>
  </Section>
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