Discriminative features in reversible stochastic attribute-value grammars

Reversible stochastic attribute-value grammars (de Kok et al., 2011) use one model for parse disambiguation and fluency ranking. Such a model encodes preferences with respect to syntax, fluency, and appropriateness of logical forms, as weighted features. Reversible models are built on the premise that syntactic preferences are shared between parse disambiguation and fluency ranking. Given that reversible models also use features that are specific to parsing or generation, there is the possibility that the model is trained to rely on these directional features. If this is true, the premise that preferences are shared between parse disambiguation and fluency ranking does not hold. In this work, we compare and apply feature selection techniques to extract the most discriminative features from directional and reversible models. We then analyse the contributions of different classes of features, and show that reversible models do rely on task-independent features.