.
Table 7. Model evaluation on relation extraction task.
['Method', 'F1', 'P', 'R']
Results. We fine-tune the BERT-based model for 25 epochs. We use F1, Precision (P) and Recall (R) measures for evaluation. Both the BERT-based baseline and our model achieve good performance, with F1 scores larger than 0.9. (2) Our model outperforms the BERT-based baseline under all settings, even when using the same information (i.e., only table metadata vs BERT-based). As one can see, our model converges much faster in comparison with the BERT-based baseline, which demonstrates that our model learns a better initialization through pre-training.