The parameters of our models are learned using AdaGrad (Duchi et al., 2011) with`2 regularization via regularized dual averaging (Xiao, 2009), and we used random search on the development set to select hyperparameters.
an alternative and related approach to learning template orderings is based on the Group Orthogonal Matching Pursuit (GOMP) algorithm for generalized linear models (Swirszcz et al., 2009;Lozano et al., 2011), with a few modifications for the setting of high-dimensional, sparse NLP data (described in appendix B).