  Recent neural network-driven semantic role labeling (SRL) systems have shown
impressive improvements in F1 scores. These improvements are due to expressive
input representations, which, at least at the surface, are orthogonal to
knowledge-rich constrained decoding mechanisms that helped linear SRL models.
Introducing the benefits of structure to inform neural models presents a
methodological challenge. In this paper, we present a structured tuning
framework to improve models using softened constraints only at training time.
Our framework leverages the expressiveness of neural networks and provides
supervision with structured loss components. We start with a strong baseline
(RoBERTa) to validate the impact of our approach, and show that our framework
outperforms the baseline by learning to comply with declarative constraints.
Additionally, our experiments with smaller training sizes show that we can
achieve consistent improvements under low-resource scenarios.
