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.

本文提出了一种结构调整框架，以在训练时通过软化约束来提高模型性能，利用神经网络的表达能力和具有结构化损失的监督学习组件，通过实验证明可以在语义角色标注任务中取得比 RoBERTa 等基线更好的结果，并在低资源情况下实现了持续改进。