Papers › Dependency or Span, End-to-End Uniform Semantic Role Labeling
Dependency or Span, End-to-End Uniform Semantic Role Labeling
Zuchao Li, Shexia He, Hai Zhao, Yiqing Zhang, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence. End-to-end SRL without syntactic input has received great attention. However, most of them focus on either span-based or dependency-based semantic representation form and only show specific model optimization respectively. Meanwhile, handling these two SRL tasks uniformly was less successful. This paper presents an end-to-end model for both dependency and span SRL with a unified argument representation to deal with two different types of argument annotations in a uniform fashion. Furthermore, we jointly predict all predicates and arguments, especially including long-term ignored predicate identification subtask. Our single model achieves new state-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL 2008, 2009) SRL benchmarks.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Role Labeling | CoNLL 2005 | Li et al. (2019) (Ensemble) | F1 | 87.7 | #9 of 15 | Archive leaderboard | report |
| Semantic Role Labeling | CoNLL 2005 | Li et al. (2019) + ELMo | F1 | 86.3 | #11 of 15 | Archive leaderboard | report |
| Semantic Role Labeling | CoNLL 2005 | Li et al. (2019) | F1 | 83.0 | #14 of 15 | Archive leaderboard | report |
| Semantic Role Labeling | OntoNotes | Li et al. | F1 | 86.0 | #11 of 17 | Archive leaderboard | report |
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