Graph Convolutions over Constituent Trees forSyntax-Aware Semantic Role Labeling
Table 2: Precision, recall and F1 on the CoNLL-2005 test sets. † indicates syntactic models and ‡ indicates multi-task learning models.
['Single', 'WSJ Test  [ITALIC] P', 'WSJ Test  [ITALIC] R', 'WSJ Test  [ITALIC] F1']
CoNLL-2005 We compare the model with state-of-the-art approaches that use syntax Strubell et al. He et al. et al. SpanGCN obtains state-of-the-art results outperforming also the multi-task self-attention model of Strubell et al. The performance on the out-of-domain data shows that SpanGCN is quite robust with nosier syntax. Emb.) we show results of models that employ contextualized embeddings on the CoNLL-2005 test set. SpanGCN performs 2.5% F1 better than its non-RoBERTa counterpart in the in-domain test set and 5.1% F1 better on the out-of-domain test set. SpanGCN is outperformed by the syntax-aware model of Wang et al. but obtains results on par with the state of the art Li et al.