{"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/improving-document-level-relation-extraction","title":"Improving Document-level Relation Extraction via Contextualizing Mention Representations and Weighting Mention Pairs","arxiv_id":null,"date":"2020-08-09","proceeding":null,"authors":["Ping Jiang;Xian-Ling Mao;Binbin Bian;Heyan Huang"],"abstract":"Document-level relation extraction (RE) has attracted considerable attention, because a large number of relational facts are expressed in multiple sentences. Recently, encoder-aggregator based models have become promising for document-level RE. However, these models have two shortcomings: (i) they cannot obtain contextualized representations of a mention by low computational cost, when the mention is involved in different entity pairs; (ii) they ignore the different weights for the mention pairs of a target entity pair. To tackle the above two problems, in this paper, we propose a novel encoder-attender-aggregator model, which introduces two attenders between the encoder and aggregator. Specifically, a mutual attender is first employed on the selected head and tail mentions to efficiently produce contextualized mention representations. Then, an integration attender is utilized to weight the mention pairs of a target entity pair. Extensive experiments on two document-level RE datasets show that the proposed model performs better than the state-of-the-art baselines. Our codes are publicly available at \"https://github.com/nefujiangping/EncAttAgg\".","url_abs":"https://ieeexplore.ieee.org/abstract/document/9194547","url_pdf":"https://conferences.computer.org/ickg/pdfs/ICKG2020-66r9RP2mQIZywMjHhQVtDI/815600a305/815600a305.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":"improving-document-level-relation-extraction","repo_url":"https://github.com/nefujiangping/EncAttAgg","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"document-level-relation-extraction","task_name":"Document-level Relation Extraction"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/relation-extraction-on-docred","task":"Relation Extraction","dataset":"DocRED","model":"EncAttAgg","rank_in_archive_order":42,"of":62,"metrics":{"F1":"58.7","Ign F1":"56.7"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}