To evaluate the performance of our approach, we compare with a standard squared loss linear regression baseline, as well as strong baselines such as linear and non-linear support vector machines (SVMs) that are widely used in text regression tasks.
by considering the semiparametric case, we not only obtain some expressiveness from the nonparametric models, but also reduce the complexity of the task: we are only interested in the finite-dimensional components Σ in the Gaussian copula with O(n log n) complexity, which is not as computationally difficult as the completely nonparametric cases.