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<Paper uid="A94-1017">
  <Title>Real-Time Spoken Language Translation Using Associative Processors</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper proposes a model using associative processors (APs) for real-time spoken language translation. Spoken language translation requires (1) an accurate translation and (2) a real-time response. We have already proposed a model, TDMT (Transfer-Driven Machine Translation), that translates a sentence utilizing examples effectively and performs accurate structural disambiguation and target word selection.</Paragraph>
    <Paragraph position="1"> This paper will concentrate on the second requirement. In TDMT, example-retrieval (ER), i.e., retrieving examples most similar to an input expression, is the most dominant part of the total processing time. Our study has concluded that we only need to implement the ER for expressions including a frequent word on APs. Experimental results show that the ER can be drastically speeded up. Moreover, a study on communications between APs demonstrates the scalability against vocabulary size by extrapolation.</Paragraph>
    <Paragraph position="2"> Thus, our model, TDMT on APs, meets the vital requirements of spoken language translation.</Paragraph>
  </Section>
class="xml-element"></Paper>