Low-Resource Semantic Role Labeling

We explore the extent to which high-resource manual annotations such as tree-banks are necessary for the task of se-mantic role labeling (SRL). We examine how performance changes without syntac-tic supervision, comparing both joint and pipelined methods to induce latent syn-tax. This work highlights a new applica-tion of unsupervised grammar induction and demonstrates several approaches to SRL in the absence of supervised syntax. Our best models obtain competitive results in the high-resource setting and state-of-the-art results in the low resource setting, reaching 72.48 % F1 averaged across lan-guages. We release our code for this work along with a larger toolkit for specifying arbitrary graphical structure.1