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<Paper uid="W03-1508">
  <Title>Transliteration of Proper Names in Cross-Lingual Information Retrieval</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Concluding Remarks
</SectionTitle>
    <Paragraph position="0"> We have presented a name transliteration procedure based on statistical machine translation techniques and have investigated its use in a cross lingual spoken document retrieval task. We have found small gains in the extrinsic evaluation of our procedure: mAP improvement from 0.501 to 0.517. In a more intrinsic and direct evaluation, we have found ways to gainfully filter a large but noisy training corpus to augment the training data for our models and improve transliteration accuracy considerably beyond our starting point, e.g., to reduce Pin-yin error rates from 51.1% to 42.5%. We expect to further refine the translation models in the future and apply them in other tasks such as text translation.</Paragraph>
  </Section>
class="xml-element"></Paper>