Paper: Low-Resource Semantic Role Labeling

ACL ID P14-1111
Title Low-Resource Semantic Role Labeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

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