Simple BERT Models for Relation Extractionand Semantic Role Labeling
Table 5: Performance comparison on span-based SRL.
['Model', 'CoNLL 05 (In-domain) P', 'CoNLL 05 (In-domain) R', 'CoNLL 05 (In-domain)  [ITALIC] F1', 'Out-of-domain (Brown) P', 'Out-of-domain (Brown) R', 'Out-of-domain (Brown)  [ITALIC] F1', 'CoNLL 12 (In-domain) P', 'CoNLL 12 (In-domain) R', 'CoNLL 12 (In-domain)  [ITALIC] F1']
We see that the BERT-LSTM-large model achieves the state-of-the-art F1 score among single models and outperforms the Ouchi et al. However, it falls short on the CoNLL 2012 benchmark because the model of Ouchi et al. They are able to achieve this with a more complex decoding layer, with human-designed constraints such as the “Overlap Constraint” and “Number Constraint”.