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Reordering Models

Reordering may depend on many factors, such as the words and phrases at the boundary of reordering points or syntactic properties. Dedicated reordering models build on these properties.

Reordering Models is the main subject of 51 publications.

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Lexicalized reordering for phrase-based models was first proposed by Tillmann (2004), a popular variant is from Koehn et al. (2005). A similar model was proposed by Ohashi et al. (2005). These models have been integrated in finite state implementations (Kumar and Byrne, 2005).
Nagata et al. (2006) extend lexicalized reordering models with better generalization by conditioning on words instead of phrases and clustering words into classes. Similarly, Xiong et al. (2006); Zens and Ney (2006) use a maximum entropy classifier to learn better reordering probabilities. Features over the source syntax tree may be included in such reordering models (Xiong et al., 2008) as well as in the translation model (Xiong et al., 2008). They may also predict reordering distance (Al-Onaizan and Papineni, 2006).
Green et al. (2010) propose a discriminatively trained reordering model. Bisazza and Federico (2013) describe discriminative reordering models to cope with long word reordering phenomena.

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