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

Abstract

Semantic role labeling is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines:1) BIO-based and 2) span-based. Despite effectiveness, 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 model to make tree structures spanaware, and further extend it to the secondorder 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.