Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
Table 2: Test set performance for different training regimes and feature sets. The models use the same decoders for testing and training. For each setting, the average and standard deviation across 5 runs with different random initializations are reported. Boldface: best (averaged) result per dataset/measure.
['Model', 'Training', 'Features', 'PTB UAS (%)', 'PTB UEM (%)', 'CTB UAS (%)', 'CTB UEM (%)']
All models use the same decoder for testing as during the training process. Though no global decoder for the arc-standard system has been explored in this paper, its local models are listed for comparison. We also include an edge-factored graph-based model, which is conventionally trained globally. The edge-factored model scores bi-LSTM features for each head-modifier pair; a maximum spanning tree algorithm is used to find the tree with the highest sum of edge scores.