{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:47:01Z","timestamp":1775029621348,"version":"3.50.1"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020AAA0106400"],"award-info":[{"award-number":["2020AAA0106400"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61976211, 61922085, 62176257"],"award-info":[{"award-number":["61976211, 61922085, 62176257"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA27020200"],"award-info":[{"award-number":["XDA27020200"]}]},{"name":"CCF-Tencent Open Research Fund, the Youth Innovation Promotion Association CAS"},{"name":"Yunnan Provincial Major Science and Technology Special Plan Projects","award":["202103AA080015"],"award-info":[{"award-number":["202103AA080015"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,3,31]]},"abstract":"<jats:p>Most existing event extraction works mainly focus on extracting events from one sentence. However, in real-world applications, arguments of one event may scatter across sentences and multiple events may co-occur in one document. Thus, these scenarios require document-level event extraction (DEE), which aims to extract events and their arguments across sentences from a document. Previous works cast DEE as a two-step paradigm: sentence-level event extraction (SEE) to document-level event fusion. However, this paradigm lacks integrating document-level information for SEE and suffers from the inherent limitations of error propagation. In this article, we propose a multi-turn and multi-granularity reader for DEE that can extract events from the document directly without the stage of preliminary SEE. Specifically, we propose a new paradigm of DEE by formulating it as a machine reading comprehension task (i.e., the extraction of event arguments is transformed to identify the answer span from the document). Beyond the framework of machine reading comprehension, we introduce a multi-turn and multi-granularity reader to capture the dependencies between arguments explicitly and model long texts effectively. The empirical results demonstrate that our method achieves superior performance on the MUC-4 and the ChFinAnn datasets.<\/jats:p>","DOI":"10.1145\/3542925","type":"journal-article","created":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T22:37:16Z","timestamp":1654987036000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-Turn and Multi-Granularity Reader for Document-Level Event Extraction"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2310-8391","authenticated-orcid":false,"given":"Hang","family":"Yang","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5485-9916","authenticated-orcid":false,"given":"Yubo","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6083-8433","authenticated-orcid":false,"given":"Kang","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-2263","authenticated-orcid":false,"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1159-5930","authenticated-orcid":false,"given":"Zuyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5494-5171","authenticated-orcid":false,"given":"Weijian","family":"Sun","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1159"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-2311"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-3006"},{"key":"e_1_3_2_5_2","first-page":"811","volume-title":"Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing","author":"Chen Pei","year":"2020","unstructured":"Pei Chen, Hang Yang, Kang Liu, Ruihong Huang, Yubo Chen, Taifeng Wang, and Jun Zhao. 2020. 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