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<Paper uid="W04-3245">
  <Title>From Machine Translation to Computer Assisted Translation using Finite-State Models</Title>
  <Section position="7" start_page="11" end_page="11" type="concl">
    <SectionTitle>
5 Conclusions and future work
</SectionTitle>
    <Paragraph position="0"> Finite-state transducers have been successfully applied to CAT. These models can be learnt from parallel corpora. The concept of interactive search has been introduced in this paper along with some efficient techniques (word graph derivation and a16 best) that solve the parsing problem given a prefix of the target sentence under real-time constraints.</Paragraph>
    <Paragraph position="1"> The results show that the 5-best approach clearly improves the quality of the translations, with respect to the 1-best approximation.</Paragraph>
    <Paragraph position="2"> The promising results achieved in the first experiments provide a new field in machine translation still to be explored, in which the human expertise is combined with machine translation techniques to increase productivity without sacrifying high-quality translation.</Paragraph>
    <Paragraph position="3"> Finally, the introduction of morpho-syntactic information or bilingual categories in finite-state transducers, are topics that leave an open door to future research. As well as some improvements in the search algorithms to reduce the computational cost of finding a path in the word graph with the minimum edit cost.</Paragraph>
  </Section>
class="xml-element"></Paper>