  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
considering internal argument structures, potentially hindering the model's
expressiveness. The key challenge is arguments are flat structures, and there
are no determined subtree realizations for words inside arguments. To remedy
this, in this paper, we propose to regard flat argument spans as latent
subtrees, accordingly reducing SRL to a tree parsing task. 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. We
conduct extensive experiments on CoNLL05 and CoNLL12 benchmarks. Results reveal
that our methods perform favorably better than all previous syntax-agnostic
works, achieving new state-of-the-art under both end-to-end and w/ gold
predicates settings.
