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<Paper uid="W98-1434">
  <Title>System Demonstration Multilingual Weather Forecast Generation System</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The MLWFA (Multilingual Weather Forecasts Assistant) system will be demonstrated. It is developed to generate the multilingual text of the weather forecasts automatically. The raw data from the weather observation can be used to generate the weather element chart. According to the weather change trend, the forecasters can directly modify the value Of the element on the chart, such as the center .point value, the isoline and the isocircle. After *that, the modified data are stored as the input for the system. The system can select a schema depending on the input or the requirement from the users. The schema library can be conveniently maintained, such as the schema modification or extension. Through optimizing and mapping the schema *tree, the microplanner constructs the brief and coherent internal text structure for the surface generator.</Paragraph>
    <Paragraph position="1"> After the processing of the generator, the muitilingual *weather forecasts used for the broadcast program are generated.</Paragraph>
    <Paragraph position="2"> Keywords: Multilingual Generation, Weather Forecast Assistant.</Paragraph>
    <Paragraph position="3"> ., 1 Introduction The MLWFA ~system is developed as the first application of the project ACNLG \[Huang et al. 97a &amp; b\] which is an international *cooperation between the German Research Center for Artificial Intelligence (DFKI) and Shanghai**Jiao Tong University (SJTU). The system mainly consists of four components: the graphic processor, the macroplanner, the microplanner and the surface generator. The graphic, *processor is used to adjust weather forecasts data by the forecasters. The technique *adopted for the macroplanner is based on the schema approach \[McKeown 85\], but we expand the operator of schema. The microplanner is based on the sentence structure optimizing which is independent of the language and language resource mapping -which is associated with the language. On the basis of the FB-LTAG (Feature-based Lexicalized -Tree Adjoining Grammar) \[Joshi 85, XTAGRG 95\], the surface generator identifies the feature of the nodes, compounds the grammar-trees and finally generates Chinese, *English and German weather forecasts.</Paragraph>
  </Section>
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