Neural Semantic Role Labeling with Dependency Path Embeddings
This work addresses a specific challenge in natural language processing for tasks requiring accurate semantic role labeling, though it appears incremental as it builds on existing neural techniques.
The paper tackles the problem of semantic role labeling by developing a model that uses neural sequence modeling to handle complex syntactic structures like nested subordinations and nominal predicates, treating them as sub-sequences of lexicalized dependency paths. The result is an improvement over previous state-of-the-art methods, with qualitative enhancements demonstrated experimentally.
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.