Cross-lingual Semantic Parsing
Table 2: Evaluation of coreference results on the test dataset. We force the model to decode the gold target sequence, and only evaluate algorithms for solving coreference. The Avg. F1 is computed by averaging F1 scores of MUC, B3 and CEAFe.
['[EMPTY]', 'MUC Prec.', 'MUC Rec.', 'MUC F1', 'B3 Prec.', 'B3 Rec.', 'B3 F1', 'CEAF [ITALIC] e Prec.', 'CEAF [ITALIC] e Rec.', 'CEAF [ITALIC] e F1', 'Avg. F1']
Since coreference in our setup occurs at the sentence level, the proposed copying mechanism achieves high scores in all three metrics, and the average F1 is 97.82. The heuristic baseline, which solves coreference only based on syntactic heads, also achieves a relatively high average F1 of 81.94. Under the MUC metric, the copying mechanism performs significantly better than the heuristic baseline. The random baseline limits the choice of coreference to preceding syntactic heads, ignoring all other tokens, achieving scores much lower than the other two approaches in all three metrics.