Neural Simile Recognition with Cyclic Multitask Learning and Local Attention
Table 2: Experimental results on the validation set with different execution number K, where standard MTL results are shown when K=0.
['[ITALIC] K', '[BOLD] F1-score for ➀', '[BOLD] F1-score for ➁']
There are large improvements for both simile classification (task ➀) and simile component recognition (task ➁) when increasing K from 0 to 1, showing the effectiveness of stacking the subtasks into a loop. Further increasing K from 1 to 2 only results in marginal improvements for both subtasks while introducing more running time, and their performances slightly go down when enlarging K from 2 to 4. All the evidence above indicates that our cyclic framework converges quickly, making it more practically useful. Considering both efficiency and performance, we set K=1 for all experiments thereafter.