Semantic Role Labeling as Syntactic Dependency Parsing

• URL: http://arxiv.org/abs/2010.11170v1
• Date: Wed, 21 Oct 2020 17:46:11 GMT
• Title: Semantic Role Labeling as Syntactic Dependency Parsing
• Authors: Tianze Shi, Igor Malioutov, Ozan \.Irsoy
• Abstract summary: Three common syntactic patterns account for over 98% of the PropBank-style semantic role labeling annotations. We present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels.
• Score: 19.919191146167584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We reduce the task of (span-based) PropBank-style semantic role labeling (SRL) to syntactic dependency parsing. Our approach is motivated by our empirical analysis that shows three common syntactic patterns account for over 98% of the SRL annotations for both English and Chinese data. Based on this observation, we present a conversion scheme that packs SRL annotations into dependency tree representations through joint labels that permit highly accurate recovery back to the original format. This representation allows us to train statistical dependency parsers to tackle SRL and achieve competitive performance with the current state of the art. Our findings show the promise of syntactic dependency trees in encoding semantic role relations within their syntactic domain of locality, and point to potential further integration of syntactic methods into semantic role labeling in the future.

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