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<Paper uid="P06-2107">
  <Title>translation</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Computers have become an important tool to increase the translator's productivity. In a more extended framework, a machine translation (MT) system can be used to obtain initial versions of the translations. Unfortunately, the state of the art in MT is far from being perfect, and a human translator must edit this output in order to achieve high-quality translations.</Paragraph>
    <Paragraph position="1"> Another possibility is computer-assisted translation (CAT). In this framework, a human translator interacts with the system in order to obtain high-quality translations. This work follows the approach of interactive CAT initially suggested by (Foster et al., 1996) and developed in the TransType2 project (SchlumbergerSema S.A. et al., 2001; Barrachina et al., 2006). In this framework, the system suggests a possible translation of a given source sentence. The human translator can accept either the whole suggestion or accept it only up to a certain point (that is, a character prefix of this suggestion). In the latter case, he/she can type one character after the selected prefix in order to direct the system to the correct translation.</Paragraph>
    <Paragraph position="2"> The accepted prefix and the new corrected character can be used by the system to propose a new suggestion to complete the prefix. The process is repeated until the user completely accepts the suggestion proposed by the system. Figure 1 shows an example of a possible CAT system interaction.</Paragraph>
    <Paragraph position="3"> Statistical machine translation (SMT) is an adequate framework for CAT since the MT models used can be learnt automatically from a training bilingual corpus and the search procedures developed for SMT can be adapted efficiently to this new interactive framework (Och et al., 2003).</Paragraph>
    <Paragraph position="4"> Phrase-based models have proved to be very adequate statistical models for MT (Tom'as et al., 2005). In this work, the use of these models has been extended to interactive CAT.</Paragraph>
    <Paragraph position="5"> The organization of the paper is as follows.</Paragraph>
    <Paragraph position="6"> The following section introduces the statistical approach to MT and section 3 introduces the statistical approach to CAT. In section 4, we review the phrase-based translation model. In section 5, we describe the decoding algorithm used in MT, and how it can be adapted to CAT. Finally, we will present some experimental results and conclusions. null</Paragraph>
  </Section>
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