Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks
Table 1: The performance of our model (w/o global node) with different discount coefficients η of loss L∗(θ) on the English development set. EM denotes the ratio of exact match on propositions.
['[ITALIC] η', '[BOLD] # of Iters', '[BOLD] P', '[BOLD] R', '[BOLD] F', '[BOLD] EM']
The model achieves the best performance when η equals to 0. It implies that adding uniqueness constraint on loss actually hurts the performance. Thus, we use the loss L∗(θ) with η equals to 0 in the rest of the experiments, which is equivalent to the loss L(θ) in Eq We also observe that the model with 2 refinement iterations performs the best (89.92% F1).