Phrase Translation Model Enhanced with Association based Features

In this paper, we propose to enhance the phrase translation model with association measures as new feature functions. These features are estimated on counts of phrase pair co-occurrence and their marginal counts. Four feature functions, namely, Dice coefficient, log-likelihood-ratio, hyper-geometric distribution and link probability are exploited and compared. Experimental results demonstrate that the performance of the phrase translation model can be improved by enhancing it with these association based feature functions. Moreover, we study the correlation between the features to predict the usefulness of a new association feature given the existing features.