{"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/graph-neural-networks-with-generated","title":"Graph Neural Networks with Generated Parameters for Relation Extraction","arxiv_id":"1902.00756","date":"2019-02-02","proceeding":"ACL 2019 7","authors":["Hao Zhu","Yankai Lin","Zhiyuan Liu","Jie Fu","Tat-Seng Chua","Maosong Sun"],"abstract":"Recently, progress has been made towards improving relational reasoning in\nmachine learning field. Among existing models, graph neural networks (GNNs) is\none of the most effective approaches for multi-hop relational reasoning. In\nfact, multi-hop relational reasoning is indispensable in many natural language\nprocessing tasks such as relation extraction. In this paper, we propose to\ngenerate the parameters of graph neural networks (GP-GNNs) according to natural\nlanguage sentences, which enables GNNs to process relational reasoning on\nunstructured text inputs. We verify GP-GNNs in relation extraction from text.\nExperimental results on a human-annotated dataset and two distantly supervised\ndatasets show that our model achieves significant improvements compared to\nbaselines. We also perform a qualitative analysis to demonstrate that our model\ncould discover more accurate relations by multi-hop relational reasoning.","url_abs":"http://arxiv.org/abs/1902.00756v1","url_pdf":"http://arxiv.org/pdf/1902.00756v1.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":"graph-neural-networks-with-generated","repo_url":"https://github.com/thunlp/gp-gnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1902.00756","atlas_url":"https://app.syntology.ai/?focus=1902.00756","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}