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 semantic role labeling (SRL). We examine how performance changes without syntactic supervision, comparing both joint and pipelined methods to induce latent syntax. This work highlights a new application 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 languages. We release our code for this work along with a larger toolkit for specifying arbitrary graphical structure.(1)