A Transition-based Algorithm for Unrestricted AMR Parsing
Table 4: F-score comparison with F flanigan2014discriminative, W wang2015transition, F’ flanigan2016cmu, D damonte-cohen-satta:2017:EACLlong, P peng-aaai18. D, P and our system are left-to-right transition-based.
['[BOLD] Metric', '[BOLD] F', '[BOLD] W', '[BOLD] F’', '[BOLD] D', '[BOLD] P', '[BOLD] Ours']
amr-covington obtains state-of-the-art results for all the standard metrics. It outperforms the rest of the models when handling reentrant edges. It is worth noting that D requires an additional classifier to handle a restricted set of reentrancy and P uses up to five classifiers to build the graph.