Deep Semantic Role Labeling with Self-Attention
Table 3: Detailed results on the CoNLL-2005 development set. PE denotes the way to encoding word positions. GloVe refers to the GloVe embedding pre-trained on 6B tokens.
['CoNLL-2005 Dataset #', 'CoNLL-2005 Dataset Nonlinearity', 'CoNLL-2005 Dataset PE', 'CoNLL-2005 Dataset Embedding', 'CoNLL-2005 Dataset Width', 'CoNLL-2005 Dataset Depth', 'CoNLL-2005 Dataset F1']
Previous works Our observations also coincide with previous works. For DeepAtt with 4 layers, our model only achieves 79.9 F1 score. Increasing depth consistently improves the performance on the development set, and our best model consists of 10 layers. For DeepAtt with 12 layers, we observe a slightly performance drop of 0.1 F1.