Joint Extraction of Entities and Relations with a Hierarchical Multi-task Tagging Model
Table 3: Ablation test on NYT10 and NYT11. We run the model for 10 times and report the average results.
['Method', 'NYT10  [ITALIC] Prec.', 'NYT10  [ITALIC] Rec.', 'NYT10  [ITALIC] F1', 'NYT11  [ITALIC] Prec.', 'NYT11  [ITALIC] Rec.', 'NYT11  [ITALIC] F1']
To validate our argument about the effectiveness of our hierarchical multi-task tagging model, we further conduct a set of ablation experiments. We remove the EE task module from our HMT model to test its performance. To make it comparable, we keep all the hype-parameters unchanged and retrain the rest part from scratch. These observations demonstrate that the learned knowledge in EE task is successfully shared and is indeed of benefit to the JE task. It also validates the capability of our model in capturing the interdependency between the two tasks.