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<Paper uid="W97-0407">
  <Title>Using Categories in the EUTRANS System</Title>
  <Section position="10" start_page="49" end_page="53" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> In the EUTRANS project, Subsequential Transucers are used as the basis of translation systems that accept speech and text input. They can be  corpus. The sizes in the horizontal axis refer to the first three columns in Table 3(b). automatically learned from corpora of examples.</Paragraph>
    <Paragraph position="1"> This learning process can be improved by means of categories using the approach detailed in this paper.</Paragraph>
    <Paragraph position="2"> Experimental results show that this approach reduces the number of examples required for achieving good models, with good translation results in acceptable times without using speciaiised hardware.</Paragraph>
    <Paragraph position="3"> Our current work concentrates in further reducing the number of examples necessary for training the translation models in order to cope with spontaneous instead of synthetic sentences. For this, new approaches are being explored, like reordering the words in the translations, the use of new inference algorithms, and automatic categorization. Results obtained with a different enhancement of our text input system, the inclusion of error correcting techniques, can be found in (Amengual et al., 1997b).</Paragraph>
  </Section>
class="xml-element"></Paper>