Papers › A Span Selection Model for Semantic Role Labeling
A Span Selection Model for Semantic Role Labeling
Hiroki Ouchi, Hiroyuki Shindo, Yuji Matsumoto
We present a simple and accurate span-based model for semantic role labeling (SRL). Our model directly takes into account all possible argument spans and scores them for each label. At decoding time, we greedily select higher scoring labeled spans. One advantage of our model is to allow us to design and use span-level features, that are difficult to use in token-based BIO tagging approaches. Experimental results demonstrate that our ensemble model achieves the state-of-the-art results, 87.4 F1 and 87.0 F1 on the CoNLL-2005 and 2012 datasets, respectively.
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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 | BiLSTM-Span (Ensemble, predicates given) | F1 | 88.5 | #7 of 15 | Archive leaderboard | report |
| Semantic Role Labeling | CoNLL 2005 | BiLSTM-Span | F1 | 87.6 | #10 of 15 | Archive leaderboard | report |
| Semantic Role Labeling | OntoNotes | BiLSTM-Span (Ensemble) | F1 | 87.0 | #9 of 17 | Archive leaderboard | report |
| Semantic Role Labeling | OntoNotes | BiLSTM-Span | F1 | 86.2 | #10 of 17 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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