Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

ACL 2018

Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

Jan 28, 2021
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Abstract: Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an end-to-end approach for jointly pre-dicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span repre-sentations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold predicates. Authors: Luheng He, Kenton Lee, Omer Levy, Luke Zettlemoyer (University of Washington)

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