Improving DRS Parsing with Separately Predicted Semantic Roles

This paper addresses Semantic Role Labeling (SRL) within the context of English Discourse Representation Structure (DRS) parsing. In particular, we investigate whether semantic roles predicted by a near-state-of-the-art SRL model can be used to improve the outputs of modern end-to-end neural DRS parsers using a rule-based post-processing algorithm.We compare two methods of generating training data for the SRL model from the Parallel Meaning Bank, one DRS-based and one CCG-based. We also compare two different post-processing algorithms. Our results vary across different DRS parsers, but overall we find a small to moderate improvement of up to 0.5 F1 on the final DRSs. We find a small but consistent advantage of DRS-based over CCG-based training data generation, and of token-based over concept-based post-processing, where applicable