We hypothesize that by leveraging syntactic structure while training neural SRL models, we may achieve robust performance, especially for low resource scenarios.
we propose to leverage syntactic parse trees as hard constraints for the SRL task i.e., we explicitly enforce that the predicted argument spans of the SRL network must agree with the spans implied by the syntactic parse of the sentence via scoring function in the training objective.