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This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. It has not been submitted in whole or in part for a degree at any other university. Some of the work has been published previously in conference proceedings (Blackwood et al., 2008a; Blackwood

We present a method for incorporating arbitrary context-informed word attributes into statistical machine translation by clustering attribute-quali ed source words, and smoothing their word translation probabilities using binary decision trees. We describe two ways in which the decision trees are used in machine translation: by using the attribute-quali ed source word clusters directly, or by using attributedependent lexical translation probabilities that are obtained from the trees, as a lexical smoothing feature in the decoder model. We present experiments using Arabic-to-English newswire data, and using Arabic diacritics and part-ofspeech as source word attributes, and show that the proposed method improves on a state-of-the-art translation system. 1

This degree is submitted to the University of Cambridge