Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

Luheng He; Kenton Lee; Omer Levy; Luke Zettlemoyer

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arXiv · 1805.04787

Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

Luheng He·Kenton Lee·Omer Levy·Luke Zettlemoyer

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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 predicting 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 representations 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.

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BibTeX RIS

Luheng He, Kenton Lee, Omer Levy, Luke Zettlemoyer. 2018-05-12. Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling. https://arxiv.org/abs/1805.04787

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