Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers
Table 4: Comparisons on the CoNLL test set. The F1 gains that are statistically significant: (1) “+EPM” compared to “top-pairs”, “ranking” and “JIM”, (2) “+EPM” compared to “reinforce” based on MUC, B3 and LEA, (3) “single” compared to “+EPM” based on MUC and B3, and (4) “ensemble” compared to other systems. Significance is measured based on the approximate randomization test (p<0.05) Noreen (1989).
['[EMPTY]', '[EMPTY]', 'MUC R', 'MUC P', 'MUC F1', '<italic>B</italic>3 R', '<italic>B</italic>3 P', '<italic>B</italic>3 F1', 'CEAF<italic>e</italic> R', 'CEAF<italic>e</italic> P', 'CEAF<italic>e</italic> F1', 'CoNLL', 'LEA R', 'LEA P', 'LEA F1']
The performance of the "+EPM" model compared to recent state-of-the-art coreference models on the CoNLL test set is presented in Table 4. [CONTINUE] EPM feature-values result in significantly better performance than those of JIM while the number of EPM feature-values is considerably less than JIM.