Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents
Table 4: Ablation results of the MRL-CoType for end-to-end relation extraction.
['[BOLD] Settings', '[BOLD] Wiki-KBP  [BOLD] Precision(%)', '[BOLD] Wiki-KBP  [BOLD] Recall(%)', '[BOLD] Wiki-KBP  [BOLD] F1(%)', '[BOLD] BioInfer  [BOLD] Precision(%)', '[BOLD] BioInfer  [BOLD] Recall(%)', '[BOLD] BioInfer  [BOLD] F1(%)']
The curriculum setting and the joint setting achieve much better results than the separate training setting. This shows the superiority of cooperative multi-agents over single view extraction, which evaluates confidences with limited information. Besides, the curriculum setting achieves better results than the joint setting, especially on the BioInfer data, which has a larger type set and is more challenging than Wiki-KBP. This indicates the effectiveness of the curriculum learning strategy, which enhances the model ability to handle large state space with gradual exploration.