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<Paper uid="C00-2174">
  <Title>Chinese Generation in a Spoken Dialogue Translation System</Title>
  <Section position="2" start_page="0" end_page="1141" type="intro">
    <SectionTitle>
1,, Introduction
</SectionTitle>
    <Paragraph position="0"> In this paper, we will present the core aspects of the generation component of our speech to speech dialogue translation system, the domain of which is hotel reservation. The whole system consists of five modules: speech recognizen translator, dialogue manageh generator and speech synthesizer. And the system takes the interlingua method in order to achieve multilinguality. Here the interlingua is an underspecified selnantic representation (USR). And the target language is Chinese in this paper.</Paragraph>
    <Paragraph position="1"> Reiter (Reiter 1995) made a clear distinction between templates and deep generation. The template method is rated as efficient but inflexible, while deep generation method is considered as flexible but inefficient. So the hybrid method to combine both the methods has been adopted in the last few years. Busemann (Busemann 1996) used hybrid method to allow template, canned texts and general rules appearing in one formalism and to tackle the problem of the inefficiency of the grammar-based surface generation system. Pianta (Pianta 1999) used the mixed representation approach to allow the system to choose between deep generation technology and template method.</Paragraph>
    <Paragraph position="2"> Our system keeps the surface generation module general for Chinese. At the same time, we can also deal with templates in tile input without changing tile whole generation process. If tile attribute in the feature structure is &amp;quot;template&amp;quot;, then the value must be taken as a word string, which will appear in the output without modification. The surface generation module assumes the input as a predicate-argument structure, which is called intermediate representation here. And any input of it must be first converted into an intermediate representation. The whole generation process can be modularized fimher into two separate components: microplanner and syntactic realizer. The microplanner is task-oriented. The input is an USR and the function of it is to plan an utterance on a phrase- or sentence-level. It maps concepts defined in the domain to a functional representation which is used by the syntactic generation components to realize an appropriate surface string for it. The functional description is made of feature structures, the attribute-value  pairs. And the functional representation serves as the intermediate representation between the microplanner and the syntactic generator. The intermediate representation is fully instantiated. This enables the surface realizer to traverse the input in a top-down, depth-first fashion to work out a grammatically correct word string for the input, which in turn speeds the whole generatiou procedure. So our system use a task-oriented microplanner and a general surface realizer. The main advantage is that it is easy to adapt the system to other domains and maintain the flexibility of the system.</Paragraph>
    <Paragraph position="3"> In this paper, section 2 gives a brief description of our semantic representation.</Paragraph>
    <Paragraph position="4"> Section 3 presents our method on the microplanning procedure. Section 4 describes the syntactic generation module. Section 5 presents the preliminary results of our generation system. Section 6 presents discussions and future work.</Paragraph>
  </Section>
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