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<Paper uid="W03-1003">
  <Title>Cross[?]Language Information Retrieval Cross[?]Language Unigram Model Contemporaneous English Articles Baseline Chinese Acoustic Model Baseline Chinese Language Model Chinese DictionaryASR Automatic Transcription English Article Aligned with Mandarin Story Machine TranslationStatistical Translation lexicon</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We propose new methods to take advantage of text in resource-rich languages to sharpen statistical language models in resource-deficient languages. We achieve this through an extension of the method of lexical triggers to the cross-language problem, and by developing a likelihood-based adaptation scheme for combining a trigger model with an a1 -gram model.</Paragraph>
    <Paragraph position="1"> We describe the application of such language models for automatic speech recognition. By exploiting a side-corpus of contemporaneous English news articles for adapting a static Chinese language model to transcribe Mandarin news stories, we demonstrate significant reductions in both perplexity and recognition errors. We also compare our cross-lingual adaptation scheme to monolingual language model adaptation, and to an alternate method for exploiting cross-lingual cues, via cross-lingual information retrieval and machine translation, proposed elsewhere.</Paragraph>
  </Section>
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