Tackling Error Propagation through Reinforcement Learning:A Case of Greedy Dependency Parsing
Table 2: Comparing training methods on PENN Treebank (arc-standard and arc-eager) and German part of SPMRL-2014 (swap-standard).
['[EMPTY]', '[BOLD] Arc-  [BOLD] standard', '[BOLD] Arc-  [BOLD] standard', '[BOLD] Arc-  [BOLD] eager', '[BOLD] Arc-  [BOLD] eager', '[BOLD] Swap-  [BOLD] standard', '[BOLD] Swap-  [BOLD] standard']
Although we used \newcitechen.manning.2014’s pretrained model and Stanford open-source software, the results of our baseline are slightly worse than what is reported in their paper. This could be due to minor differences in settings and does not affect our conclusions. The usefulness of training examples that contain errors is evident through the better performance of RL-Random and RL-Memory in comparison to RL-Oracle.