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 propose Maximum Ranking Correlation (MRC) as an objective function in discriminative tuning of parameters in a linear model of Statistical Machine Translation (SMT). We try to maximize the ranking correlation between sentence level BLEU (SBLEU) scores and model scores of the N-best list, while the MERT paradigm focuses on the potential 1-best candidates of the N-best list. After we optimize the MER and the MRC objectives using an multiple objective optimization algorithm at the same time, we interpolate them to obtain parameters which outperform both. Experimental results on WMT French–English data set confirm that our method significantly outperforms MERT on out-of-domain data sets, and performs marginally better than MERT on in-domain data sets, which validates the usefulness of MRC on both domain specific and general domain data.