A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling
Table 2: Results on the English in-domain test set.
['System', 'P', 'R', 'F1']
For English, our model significantly outperformed all the local counter-parts (i.e., models which do not perform global inference) on the in-domain tests (see with 87.6% F1 for our model vs. 86.7% for PathLSTM Roth and Lapata When compared with global models, our model performed on-par with the state-of-the-art global version of PathLSTM.