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<Paper uid="P98-2141">
  <Title>Simultaneous Interpretation Utilizing Example-based Incremental Transfer</Title>
  <Section position="1" start_page="0" end_page="855" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper describes a practical method of automatic simultaneous interpretation utilizing an example-based incremental transfer mechanism. We primarily show how incremental translation is achieved in the context of an example-based framework. We then examine the type of translation examples required for a simultaneous interpretation to create naturally communicative dialogs. Finally, we propose a scheme for automatic simultaneous interpretation exploiting this example-based incremental translation mechanism. Preliminary experimentation analyzing the performance of our example-based incremental translation mechanism leads us to believe that the proposed scheme can be utilized to achieve a practical simultaneous interpretation system.</Paragraph>
    <Paragraph position="1"> Introduction Speech-to-speech translation necessitates quick and perspicuous responses to natural communication. Furthermore, since dialogues continuously expand, it is essential to incrementally translate inputs to avoid interrupting the coherency of communications.</Paragraph>
    <Paragraph position="2"> Therefore, a high degree of incrementality and acceptability in translation such as simultaneous interpretation is essential. To satisfy these requirements, an incremental translation system, which functions as a simultaneous interpreter, is seen as an efficient solution in this field.</Paragraph>
    <Paragraph position="3"> The main characteristic of incremental translations is the translation process. This is activated synchronously with the input, in contrast with conventional sentence-by-sentence-based translation which cannot start processing until the end of an input (Kitano, 1994). However, in incremental translation, we believe that the following issues must be resolved to achieve actual simultaneous interpretation : * How to define Information Units (IUs) (Halliday, 1994) to determine appropriate components for translation - Since differences exist among the word order of various languages, especially between linguistically distant languages such as English and Japanese, appropriate transfer units, equally effective for both the source and target languages, have to be defined.</Paragraph>
    <Paragraph position="4"> * How to determine plausible translation for each IU - In terms of the information content, the greater the number of words contained in IUs, the less semantic ambiguity in translation, or the later the response is obtained. Because of time restrictions, deterministic processing by exploiting specious measures (e.g. linguistical or statistical plausibility) is required for each IU translation in order to shorten the length of IUs.</Paragraph>
    <Paragraph position="5"> * How to install simultaneous interpreters' know-how (i.e. empirical knowledge) - In practical simultaneous interpretation, human translators generally use strong sentence planning using particular empirical know-how.</Paragraph>
    <Paragraph position="6"> The exploitation of this kind of knowledge is essential for achieving practical simultaneous interpretation (Kitano, 1994).</Paragraph>
    <Paragraph position="7"> Transfer-Driven Machine Translation (TDMT) (Furuse, 1994a) (Mima, 1997) has been proposed, and an efficient method of spoken dialog  translation. TDMT has the following key features: * Utilization of Constituent Boundary Patterns (CB-Patterns) (Fumse, 1994b) (Furuse, 1996) - CB-Patterns based on meaningful information  units are applied to parse an input incrementally and produce translations based on the synchronization of the source and target language structure pairs (Abeillr, 1990) (Shieber, 1990). This contrasts with the linguistic manner of applying grammar rules.</Paragraph>
    <Paragraph position="8"> The result of this provides for incremental translations that can even handle lengthy input  * Current affiliation: Department of Computing, Manchester Metropolitan University, Manchester M 1 5GD, U.K. ~&amp;quot; Current affiliation: NTT Communication Science Laboratories, 2-4 Hikaridai Seika-cho Soraku-gun, Kyoto 6190237, Japan.</Paragraph>
    <Paragraph position="9">  efficiently by splitting the input into appropriate and meaningful chunks. In addition, * Existence of efficient disambiguation scheme - by dealing with best-only substructures  utilizing stored empirical translation examples compiled from a linguistic database, the explosion of structural ambiguities is significantly constrained (Furuse, 1996).</Paragraph>
    <Paragraph position="10"> Accordingly, TDMT has the advantage of having both the capability to define effective IU and an efficient deterministic processing scheme in incremental spoken-language translation.</Paragraph>
    <Paragraph position="11"> Additionally, in exploiting the empirical &amp;quot;knowledge that is required in practical simultaneous interpretation, we can assume that the empirical knowledge is described within the linguistic resource of simultaneous interpretation corpora. (Harbusch, 1992) proposed a method of default handling in incremental generation based on this observation.</Paragraph>
    <Paragraph position="12"> in this paper, we describe the achievement of practical simultaneous interpretation using a TDMT. Furthermore, we discuss what kind of empirical knowledge is required for realizing efficient simultaneous interpretation, in terms of a simultaneous translator's knowledge, as well as proposing a method to exploit this empirical knowledge in an example-based framework in order to produce consistent translations.</Paragraph>
    <Paragraph position="13"> A preliminary experiment analyzing our proposed scheme indicates that it should be able to be used in achieving simultaneous interpretation systems.</Paragraph>
    <Paragraph position="14"> The next section of the paper briefly explains incremental translation using TDMT. Section 2 discusses the type of empirical knowledge necessary in simultaneous interpretation using some examples. Section 3 describes our proposed scheme for exploiting simultaneous interpretation examples. Section 4 presents a preliminary experiment for analyzing our proposed scheme to confirm its feasibility. Section 5 examines some related research in the field of incremental translation. Finally, a summary of our approach concludes this paper.</Paragraph>
  </Section>
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