, m} and ∀v ∈ V. The student takes the probability outputs from the experts as the ground truth and is trained in a supervised manner to minimize the cross-entropy loss L w.r.t to teachers' probability distribution: where {θ k } K k=1 are the policy parameters of experts and θ S are the student model parameters; similarly p S θ (z ij = v; x k , z i{1:j−1} ) is the probability assigned to output token z ij by student S. This training objective enables the unified parser to learn domain-specific parsing strategies from individual domains as well as leverage structural variations across domains.
the combined multi-domain policy S is refined and compressed during the distillation process thus rendering it to be more effective in parsing for each of the domains.