{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/document-level-event-extraction-via-parallel","title":"Document-level Event Extraction via Parallel Prediction Networks","arxiv_id":null,"date":"2021-08-01","proceeding":"ACL 2021 5","authors":["Hang Yang","Dianbo Sui","Yubo Chen","Kang Liu","Jun Zhao","Taifeng Wang"],"abstract":"Document-level event extraction (DEE) is indispensable when events are described throughout a document. We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document. It is a challenging task because it requires a holistic understanding of the document and an aggregated ability to assemble arguments across multiple sentences. In this paper, we propose an end-to-end model, which can extract structured events from a document in a parallel manner. Specifically, we first introduce a document-level encoder to obtain the document-aware representations. Then, a multi-granularity non-autoregressive decoder is used to generate events in parallel. Finally, to train the entire model, a matching loss function is proposed, which can bootstrap a global optimization. The empirical results on the widely used DEE dataset show that our approach significantly outperforms current state-of-the-art methods in the challenging DEE task. Code will be available at https://github.com/HangYang-NLP/DE-PPN.","url_abs":"https://aclanthology.org/2021.acl-long.492","url_pdf":"https://aclanthology.org/2021.acl-long.492.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"document-level-event-extraction-via-parallel","repo_url":"https://github.com/hangyang-nlp/de-ppn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"document-level-event-extraction-via-parallel","repo_url":"https://github.com/Spico197/DE-PPN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"document-level-event-extraction","task_name":"Document-level Event Extraction"},{"task_slug":"event-extraction","task_name":"Event Extraction"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"global-optimization","task_name":"global-optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}