SpanBERT: Improving Pre-training by Representingand Predicting Spans
Table 3: Performance on the OntoNotes coreference resolution benchmark. The main evaluation is the average F1 of three metrics: MUC, B3, and CEAFϕ4 on the test set.
['[EMPTY]', 'MUC P', 'MUC R', 'MUC F1', 'B3 P', 'B3 R', 'B3 F1', 'CEAF [ITALIC] ϕ4 P', 'CEAF [ITALIC] ϕ4 R', 'CEAF [ITALIC] ϕ4 F1', 'Avg. F1']
Our BERT reimplementation improves the Google BERT model by 1.2% on the average F1 metric and single-sequence training brings another 0.5% gain. Finally, SpanBERT improves considerably on top of that, achieving a new state of the art of 79.6% F1 (previous best result is 73.0%).