Neural-Davidsonian Semantic Proto-role Labeling
This work addresses semantic role labeling for natural language processing researchers, presenting an incremental improvement with specific gains.
The paper tackles semantic proto-role labeling by introducing a Neural-Davidsonian model using a bidirectional LSTM encoding strategy, achieving state-of-the-art results and enabling parameter sharing for learning new attribute types with limited supervision.
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.