Statistical Ranking in Tactical Generation

In this paper we describe and evaluate several statistical models for the task of realization ranking, i.e. the problem of discriminating between competing surface realizations generated for a given input semantics. Three models (and several variants) are trained and tested: an n-gram language model, a discriminative maximum entropy model using structural information (and incorporating the language model as a separate feature), and finally an SVM ranker trained on the same feature set. The resulting hybrid tactical generator is part of a larger, semantic transfer MT system.