| Models considered 2.1 Basic Conditional Random Fields | The model parameters Nd), then, form the parameters of the leaves of this hierarchy. |
| Models considered 2.1 Basic Conditional Random Fields | (3) represent the likelihood of data in each domain given their corresponding model parameters, the second line represents the likelihood of each model parameter in each domain given the hyper-parameter of its parent in the tree hierarchy of features and the last term goes over the entire tree ’2' except the leaf nodes. |
| Models considered 2.1 Basic Conditional Random Fields | We perform a MAP estimation for each model parameter as well as the hyper-parameters. |
| Challenges for Discriminative SMT | This itself provides robustness to noisy data, in addition to the explicit regularisation from a prior over the model parameters . |
| Discriminative Synchronous Transduction | Here k ranges over the model’s features, and A = {M} are the model parameters (weights for their corresponding features). |
| Discriminative Synchronous Transduction | Each L-BFGS iteration requires the objective value and its gradient with respect to the model parameters . |