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We reduce the task of (span-based) PropBank-style semantic role labeling(SRL) to syntactic dependency parsing. Our approach is motivated by ourempirical analysis that shows three common syntactic patterns account for over98% of the SRL annotations for both English and Chinese data. Based on thisobservation, we present a conversion scheme that packs SRL annotations intodependency tree representations through joint labels that permit highlyaccurate recovery back to the original format. This representation allows us totrain statistical dependency parsers to tackle SRL and achieve competitiveperformance with the current state of the art. Our findings show the promise ofsyntactic dependency trees in encoding semantic role relations within theirsyntactic domain of locality, and point to potential further integration ofsyntactic methods into semantic role labeling in the future.

Semantic Role Labeling as Syntactic Dependency Parsing