Linguistically Motivated Complementizer Choice in Surface Realization

This paper shows that using linguistically motivated features for English that-complementizer choice in an averaged perceptron model for classification can improve upon the prediction accuracy of a state-of-the-art realization ranking model. We report results on a binary classification task for predicting the presence/absence of a that-complementizer using features adapted from Jaeger’s (2010) investigation of the uniform information density principle in the context of that-mentioning. Our experiments confirm the efficacy of the features based on Jaeger’s work, including information density–based features. The experiments also show that the improvements in prediction accuracy apply to cases in which the presence of a that-complementizer arguably makes a substantial difference to fluency or intelligiblity. Our ultimate goal is to improve the performance of a ranking model for surface realization, and to this end we conclude with a discussion of how we plan to combine the local complementizer-choice features with those in the global ranking model.