An Iteratively Parallel Generation Method with the Pre-Filling Strategy for Document-level Event Extraction

In document-level event extraction (DEE) tasks, a document typically contains many event records with multiple event roles. Therefore, accurately extracting all event records is a big challenge since the number of event records is not given. Previous works present the entitybased directed acyclic graph (EDAG) generation methods to autoregressively generate event roles, which requires a given generation order. Meanwhile, parallel methods are proposed to generate all event roles simultaneously, but suffer from the inadequate training which manifests zero accuracies on some event roles. In this paper, we propose an Iteratively Parallel Generation method with the Pre-Filling strategy (IPGPF). Event roles in an event record are generated in parallel to avoid order selection, and the event records are iteratively generated to utilize historical results. Experiments on two public datasets show our IPGPF improves 11.7 F1 than previous parallel models and up to 5.1 F1 than auto-regressive models under the control variable settings. Moreover, our enhanced IPGPF outperforms other entityenhanced models and achieves new state-ofthe-art performance 1 . * Work was done when Guanhua was an intern at ByteDance AI Lab. † Corresponding author. 1 Our code is available at https://github.com/ CarlanLark/IPGPF [S6] …, Jinggong Group increased its holdings of the company's stock by 182,038 shares through the secondary market on Dec 15, 2011,… [S7] …, the shares held by Jinggong Group in the company increased from 90,880,020 shares to 91,062,058 shares, … [S9] on Dec 16, 2011, Jinggong Group reduced its holdings of ... 35,000 shares, with an average price of 19.88.