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<Paper uid="W04-3245">
  <Title>From Machine Translation to Computer Assisted Translation using Finite-State Models</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> State-of-the-art machine translation techniques are still far from producing high quality translations.</Paragraph>
    <Paragraph position="1"> This drawback leads us to introduce an alternative approach to the translation problem that brings human expertise into the machine translation scenario. (Langlais et al., 2000) proposed this idea that can be illustrated as follows. Initially, the human translator is provided with a possible translation for the sentence to be translated. Unfortunately in most of the cases, this translation is not perfect, so the translator amends it and asks for a translation of the part of the sentence still to be translated (completion). This latter interaction is repeated as many times as needed until the final translation is achieved.</Paragraph>
    <Paragraph position="2"> The scenario described in the previous paragraph, can be seen as an iterative refinement of the translations offered by the translation system, that without possessing the desired quality, help the translator to increase his/her productivity. Nowadays, this lack of translation excellence is a common characteristic in all machine translation systems.</Paragraph>
    <Paragraph position="3"> Therefore, the human-machine synergy represented by the CAT paradigm seems to be more promising than fully-automatic translation in the near future.</Paragraph>
    <Paragraph position="4"> The CAT paradigm has two important aspects: the models need to provide adequate completions and they have to do so efficiently to perform under usability constrains. To fulfill these two requirements, Stochastic Finite State Transducers (SFST) have been selected since they have proved in the past to be able to provide adequate translations (Vidal, 1997; Knight and Al-Onaizan, 1998; Amengual et al., 2000; Casacuberta et al., 2001; Bangalore and Ricardi, 2001). In addition, efficient parsing algorithms can be easily adapted in order to provide completions.</Paragraph>
    <Paragraph position="5"> The rest of the paper is structured as follows.</Paragraph>
    <Paragraph position="6"> The following section introduces the general setting for machine translation and finite state models. In section 3, the search procedure for an interactive translation is presented. Experimental results are presented in section 4. Finally, some conclusions and future work are explained in section 5.</Paragraph>
  </Section>
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