Syntactic Re-Alignment Models for Machine Translation

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Syntactic Re-Alignment Models for Machine Translation
We present a method for improving word alignment for statistical syntax-based machine translation that employs a syntactically informed alignment model closer to the translation model than commonly-used word alignment models. This leads to extraction of more useful linguistic patterns and improved BLEU scores on translation experiments in Chinese and Arabic. 1 Methods of statistical MT Roughly speaking, there are two paths commonly taken in statistical machine translation (Figure 1). The idealistic path uses an unsupervised learning algorithm such as EM (Demptser et al., 1977) to learn parameters for some proposed translation model from a bitext training corpus, and then directly translates using the weighted model. Some examples of the idealistic approach are the direct IBM word model (Berger et al., 1994; Germann et al., 2001), the phrase-based approach of Marcu and Wong (2002), and the syntax approaches of Wu (1996) and Yamada and Knight (2001). Idealistic approaches are conceptual...
Jonathan May, Kevin Knight
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Authors Jonathan May, Kevin Knight
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