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<Paper uid="P98-2221">
  <Title>Modeling with Structures in Statistical Machine Translation</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Most (if not all) statistical machine translation systems employ a word-based alignment model (Brown et al., 1993; Vogel, Ney, and Tillman, 1996; Wang and Waibel, 1997), which treats words in a sentence as independent entities and ignores the structural relationship among them.</Paragraph>
    <Paragraph position="1"> While this independence assumption works well in speech recognition, it poses a major problem in our experiments with spoken language translation between a language pair with very different word orders. In this paper we propose a translation model that employs shallow phrase structures. It has the following advantages over word-based alignment: * Since the translation model can directly depict phrase reordering in translation, it is more accurate for translation between languages with different word (phrase) orders.</Paragraph>
    <Paragraph position="2"> * The decoder of the translation system can use the phrase information and extend hypothesis by phrases (multiple words), therefore it can speed up decoding.</Paragraph>
    <Paragraph position="3"> The paper is organized as follows. In section 2, the problems of word-based alignment models are discussed. To alienate these problems, a new alignment model based on shallow phrase structures is introduced in section 3. In section 4, a grammar inference algorithm is presented that can automatically acquire the phrase structures used in the new model. Translation performance is then evaluated in section 5, and conclusions are presented in section 6.</Paragraph>
  </Section>
class="xml-element"></Paper>