Deep Semantic Role Labeling with Self-Attention
Table 5: Detailed scores on the development set of CoNLL-2005 dataset. We also list the previous state-of-the-art model [He et al.2017] for comparison.
['Label', 'He et al.\xa0he2017deep Precision', 'He et al.\xa0he2017deep Recall', 'He et al.\xa0he2017deep F1', 'DeepAtt Precision', 'DeepAtt Recall', 'DeepAtt F1']
Detailed Scores Compared with He et al. \shortcitehe2017deep, our model shows improvement on all labels except AM-PNC, where He’s model performs better. Our model improves the previous state-of-the-art on both identifying correct spans as well as correctly classifying them into semantic roles. However, the majority of improvements come from classifying semantic roles. This indicates that finding the right constituents remains a bottleneck of our model.