Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Table 3: Results on the test set for English.
['System', 'P', 'R', 'F1']
Our model is local, as all the argument detection and labeling decisions are conditionally independent: their interaction is captured solely by the LSTM+GCN encoder. This makes our model fast and simple, though, as shown in previous work, global modeling of the structured output is beneficial. We leave this extension for future work. Interestingly, we outperform even the best global model and the best ensemble of global models, without using global modeling or ensembles. When we create an ensemble of 3 models with the product-of-expert combination rule, we improve by 1.2% over the best previous result, achieving 89.1% F1.