Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments

Anonymous

Abstract: Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based.Despite ubiquity, they share some intrinsic drawbacks of not explicitly considering internal argument structures, which may potentially hinder the model's expressiveness.To remedy this, we propose to reduce SRL to a dependency parsing task and regard the flat argument spans as latent subtrees.In particular, we equip our formulation with a novel span-constrained TreeCRF to make tree structures span-aware, and further extend it to the second-order case.Experiments on CoNLL05 and CoNLL12 benchmarks reveal that the results of our methods outperform all previous works and achieve the state-of-the-art.