  Semantic role labeling (SRL) is an NLP task involving the assignment of
predicate arguments to types, called semantic roles. Though research on SRL has
primarily focused on verbal predicates and many resources available for SRL
provide annotations only for verbs, semantic relations are often triggered by
other linguistic constructions, e.g., nominalizations. In this work, we
investigate a transfer scenario where we assume role-annotated data for the
source verbal domain but only unlabeled data for the target nominal domain. Our
key assumption, enabling the transfer between the two domains, is that
selectional preferences of a role (i.e., preferences or constraints on the
admissible arguments) do not strongly depend on whether the relation is
triggered by a verb or a noun. For example, the same set of arguments can fill
the Acquirer role for the verbal predicate `acquire' and its nominal form
`acquisition'. We approach the transfer task from the variational autoencoding
perspective. The labeler serves as an encoder (predicting role labels given a
sentence), whereas selectional preferences are captured in the decoder
component (generating arguments for the predicting roles). Nominal roles are
not labeled in the training data, and the learning objective instead pushes the
labeler to assign roles predictive of the arguments. Sharing the decoder
parameters across the domains encourages consistency between labels predicted
for both domains and facilitates the transfer. The method substantially
outperforms baselines, such as unsupervised and `direct transfer' methods, on
the English CoNLL-2009 dataset.
