We present a new method for semantic role labeling in which arguments and semantic roles are jointly embedded in a shared vector space for a given predicate. These embeddings belong to a neural network, whose output represents the potential functions of a graphical model designed for the SRL task. We consider both local and structured learning methods and obtain strong results on standard PropBank and FrameNet corpora with a straightforward product-of-experts model. We further show how the model can learn jointly from PropBank and FrameNet annotations to obtain additional improvements on the smaller FrameNet dataset.
FitzGerald et al. REF proposed a method for semantic role annotation in which arguments and semantic roles were jointly embedded in a shared vector space for a given predicate.
Semantic Role Labeling with Neural Network Factors
EMNLP
Computer Science