Papers › Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling

12 May 2018ACL 2018 7arXiv:1805.04787archive 2025-07-28

Luheng He, Kenton Lee, Omer Levy, Luke Zettlemoyer

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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Tasks

Semantic Role Labeling

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Role Labeling CoNLL 2005 He et al. (2018) + ELMo F1 86.0 #13 of 15 Archive leaderboard report
Semantic Role Labeling CoNLL 2005 He et al. (2018) F1 82.5 #15 of 15 Archive leaderboard report
Semantic Role Labeling OntoNotes He et al., F1 85.5 #12 of 17 Archive leaderboard report
Semantic Role Labeling OntoNotes He et al. F1 82.1 #16 of 17 Archive leaderboard report
Semantic Role Labeling (predicted predicates) CoNLL 2005 He et al. 2018 + ELMo F1 86.0 #2 of 5 Archive leaderboard report
Semantic Role Labeling (predicted predicates) CoNLL 2005 He et al. (2018) F1 86.0 #3 of 5 Archive leaderboard report
Semantic Role Labeling (predicted predicates) CoNLL 2005 He et al. 2018 F1 82.5 #5 of 5 Archive leaderboard report
Semantic Role Labeling (predicted predicates) CoNLL 2012 He et al. 2018 + ELMo F1 82.9 #4 of 7 Archive leaderboard report
Semantic Role Labeling (predicted predicates) CoNLL 2012 He et al. 2018 F1 79.8 #7 of 7 Archive leaderboard report

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