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<Paper uid="P05-1058">
  <Title>Alignment Model Adaptation for Domain-Specific Word Alignment</Title>
  <Section position="2" start_page="0" end_page="467" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Word alignment was first proposed as an intermediate result of statistical machine translation (Brown et al., 1993). In recent years, many researchers have employed statistical models (Wu, 1997; Och and Ney, 2003; Cherry and Lin, 2003) or association measures (Smadja et al., 1996; Ahrenberg et al., 1998; Tufis and Barbu, 2002) to build alignment links. In order to achieve satisfactory results, all of these methods require a large-scale bilingual corpus for training. When the large-scale bilingual corpus is not available, some researchers use existing dictionaries to improve word alignment (Ker and Chang, 1997). However, only a few studies (Wu and Wang, 2004) directly address the problem of domain-specific word alignment when neither the large-scale domain-specific bilingual corpus nor the domain-specific translation dictionary is available. In this paper, we address the problem of word alignment in a specific domain, in which only a small-scale corpus is available. In the domain-specific (in-domain) corpus, there are two kinds of words: general words, which also frequently occur in the out-of-domain corpus, and domain-specific words, which only occur in the specific domain. Thus, we can use the out-of-domain bilingual corpus to improve the alignment for general words and use the in-domain bilingual corpus for domain-specific words. We implement this by using alignment model adaptation.</Paragraph>
    <Paragraph position="1"> Although the adaptation technology is widely used for other tasks such as language modeling (Iyer et al., 1997), only a few studies, to the best of our knowledge, directly address word alignment adaptation. Wu and Wang (2004) adapted the alignment results obtained with the out-of-domain corpus to the results obtained with the in-domain corpus. This method first trained two models and two translation dictionaries with the in-domain corpus and the out-of-domain corpus, respectively. Then these two models were applied to the in-domain corpus to get different results. The trained translation dictionaries were used to select alignment links from these different results. Thus, this method performed adaptation through result combination. The experimental results showed a significant error rate reduction as compared with the method directly combining the two corpora as training data.</Paragraph>
    <Paragraph position="2"> In this paper, we improve domain-specific word alignment through statistical alignment model adaptation instead of result adaptation. Our method includes the following steps: (1) two word alignment models are trained using a small-scale in-domain bilingual corpus and a large-scale  out-of-domain bilingual corpus, respectively. (2) A new alignment model is built by interpolating the two trained models. (3) A translation dictionary is also built by interpolating the two dictionaries that are trained from the two training corpora. (4) The new alignment model and the translation dictionary are employed to improve domain-specific word alignment results. Experimental results show that our approach improves domain-specific word alignment in terms of both precision and recall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies. The remainder of the paper is organized as follows. Section 2 introduces the statistical word alignment model. Section 3 describes our alignment model adaptation method. Section 4 describes the method used to build the translation dictionary. Section 5 describes the model adaptation algorithm. Section 6 presents the evaluation results. The last section concludes our approach.</Paragraph>
  </Section>
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