Fine-tune Bert for DocRED with Two-step Process
Table 2: Comparison of the BERT model with other baselines. We report F1 score on the Dev and Test set.
['Model', 'Dev', 'Test']
We can see that we obtain a 2% F1 improvement by using the BERT encoder, which indicates that it may contain useful information such as common-sense knowledge in order to solve this task. By using the two-step training process, performance is improved further improve. In our experiments, we find that the accuracy for the second step is above 90%, which means the bottleneck lies in the first step, e.g., predicting whether a relation exists for a given entity pair.