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.