Graph Convolutions over Constituent Trees forSyntax-Aware Semantic Role Labeling
Table 4: Results on FrameNet 1.5 test set using gold frames. † indicates syntactic models and ‡ indicates multi-task learning models.
['[BOLD] Model', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F1']
Swayamdipta et al. Like our method, this approach injects syntactic information (though dependency rather than constituent syntax) into word representations which are then used by the SRL model. The SpanGCN model obtains 69.3% F1 score. (3.8% improvement). SpanGCN also slightly outperforms (0.2% F1) the multi-task syntactic model of Swayamdipta et al.