Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Table 4: F1 scores for all event types and the averaged ones (Avg.) on single-event (S.) and multi-event (M.) sets.
['[BOLD] Model', '[BOLD] EF  [BOLD] S.', '[BOLD] EF  [BOLD] M.', '[BOLD] ER  [BOLD] S.', '[BOLD] ER  [BOLD] M.', '[BOLD] EU  [BOLD] S.', '[BOLD] EU  [BOLD] M.', '[BOLD] EO  [BOLD] S.', '[BOLD] EO  [BOLD] M.', '[BOLD] EP  [BOLD] S.', '[BOLD] EP  [BOLD] M.', '[BOLD] Avg.  [BOLD] S.', '[BOLD] Avg.  [BOLD] M.', '[BOLD] Avg.  [BOLD] S. & M.']
Single-Event vs. Multi-Event. We divide the test set into a single-event set, containing documents with just one event record, and a multi-event set, containing others, to show the extreme difficulty when arguments-scattering meets multi-event. Although Doc2EDAG still maintains the highest extraction performance for all cases, the multi-event set is extremely challenging as the extraction performance of all models drops significantly. Especially, GreedyDec, with no mechanism for the multi-event challenge, decreases most drastically. DCFEE-O decreases less, but is still far away from Doc2EDAG. On the multi-event set, Doc2EDAG increases by 17.7 F1 scores over DCFEE-O, the best baseline, on average.