A Span Selection Model for Semantic Role Labeling
2018-10-04EMNLP 2018Code Available0· sign in to hype
Hiroki Ouchi, Hiroyuki Shindo, Yuji Matsumoto
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Abstract
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| CoNLL 2005 | BiLSTM-Span (Ensemble, predicates given) | F1 | 88.5 | — | Unverified |
| CoNLL 2005 | BiLSTM-Span | F1 | 87.6 | — | Unverified |
| OntoNotes | BiLSTM-Span (Ensemble) | F1 | 87 | — | Unverified |
| OntoNotes | BiLSTM-Span | F1 | 86.2 | — | Unverified |