Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Table 3: Overall event-level precision (P.), recall (R.) and F1 scores evaluated on the test set.
['[BOLD] Model', '[BOLD] EF  [BOLD] P.', '[BOLD] EF  [BOLD] R.', '[BOLD] EF  [BOLD] F1', '[BOLD] ER  [BOLD] P.', '[BOLD] ER  [BOLD] R.', '[BOLD] ER  [BOLD] F1', '[BOLD] EU  [BOLD] P.', '[BOLD] EU  [BOLD] R.', '[BOLD] EU  [BOLD] F1', '[BOLD] EO  [BOLD] P.', '[BOLD] EO  [BOLD] R.', '[BOLD] EO  [BOLD] F1', '[BOLD] EP  [BOLD] P.', '[BOLD] EP  [BOLD] R.', '[BOLD] EP  [BOLD] F1']
Specifically, Doc2EDAG improves 19.1, 4.2, 26.5, 28.4 and 13.4 F1 scores over DCFEE-O, the best baseline, on EF, ER, EU, EO and EP events, respectively. These vast improvements mainly owe to the document-level end-to-end modeling of Doc2EDAG. Moreover, since we work on automatically generated data, the direct document-level supervision can be more robust than the extra sentence-level supervision used in DCFEE, which assumes the sentence containing most event arguments as the key-event one. This assumption does not work well on some event types, such as EF, EU and EO, on which DCFEE-O is even inferior to the most straightforward baseline, GreedyDec. Besides, DCFEE-O achieves better results than DCFEE-M, which demonstrates that naively guessing multiple events from the key-event sentence cannot work well. By comparing Doc2EDAG with GreedyDec that owns high precision but low recall, we can clearly see the benefit of document-level end-to-end modeling.