Improving Semantic Dependency Parsing with Syntactic Features

Robin Kurtz, Daniel Roxbo, and Marco Kuhlmann. Improving Semantic Dependency Parsing with Syntactic Features. In Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing, pages 12–21, Turku, Finland, 2019.


We extend a state-of-the-art deep neural architecture for semantic dependency parsing with features defined over syntactic dependency trees. Our empirical results show that only gold-standard syntactic information leads to consistent improvements in semantic parsing accuracy, and that the magnitude of these improvements varies with the specific combination of the syntactic and the semantic representation used. In contrast, automatically predicted syntax does not seem to help semantic parsing. Our error analysis suggests that there is a significant overlap between syntactic and semantic representations.