Syntax-aware Neural Semantic Role LabelingZhenghua Li is the corresponding Author.
Table 3: Comparison with previous results.
['Single', 'TPF w/ ELMo', 'WSJ  [BOLD] 86.9', 'Brown  [BOLD] 76.8', 'Combi  [BOLD] 85.6']
In the single-model scenario, the TPF approach outperforms both He et al. Using ELMo representations promotes our results as the state-of-the-art. We discuss the work of Strubell et al. Meanwhile, inspired by the success of syntactic features in traditional SRL approaches, researchers also try to enhance neural network based SRL approaches by syntax. He et al. Strubell et al. The key idea is introduce an auxiliary training objective that encourages one attention head to attend to its syntactic head word. They also use multi-task learning on POS tagging, dependency parsing, and SRL to obtain better encoding of the input sentence. Swayamdipta et al. They report +0.8 F1 improvement over their baseline SRL model. Different from Swayamdipta et al.