Neural Semantic Role Labeling with Dependency Path Embeddings

Understand

This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques.

• Our approach is motivated by the observation that complex syntactic structures and related phenomena, such as nested subordinations and nominal predicates, are not handled well by existing models.
• Our model treats such instances as sub-sequences of lexicalized dependency paths and learns suitable embedding representations.
• We experimentally demonstrate that such embeddings can improve results over previous state-of-the-art semantic role labelers, and showcase qualitative improvements obtained by our method.