  We present a model for semantic proto-role labeling (SPRL) using an adapted
bidirectional LSTM encoding strategy that we call "Neural-Davidsonian":
predicate-argument structure is represented as pairs of hidden states
corresponding to predicate and argument head tokens of the input sequence. We
demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network
naturally shares parameters between attributes, allowing for learning new
attribute types with limited added supervision.
