{"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/tpn-transferable-proto-learning-network","title":"TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction","arxiv_id":"2410.00412","date":"2024-10-01","proceeding":null,"authors":["Yu Zhang","Zhao Kang"],"abstract":"Few-shot document-level relation extraction suffers from poor performance due to the challenging cross-domain transferability of NOTA (none-of-the-above) relation representation. In this paper, we introduce a Transferable Proto-Learning Network (TPN) to address the challenging issue. It comprises three core components: Hybrid Encoder hierarchically encodes semantic content of input text combined with attention information to enhance the relation representations. As a plug-and-play module for Out-of-Domain (OOD) Detection, Transferable Proto-Learner computes NOTA prototype through an adaptive learnable block, effectively mitigating NOTA bias across various domains. Dynamic Weighting Calibrator detects relation-specific classification confidence, serving as dynamic weights to calibrate the NOTA-dominant loss function. Finally, to bolster the model's cross-domain performance, we complement it with virtual adversarial training (VAT). We conduct extensive experimental analyses on FREDo and ReFREDo, demonstrating the superiority of TPN. Compared to state-of-the-art methods, our approach achieves competitive performance with approximately half the parameter size. Data and code are available at https://github.com/EchoDreamer/TPN.","url_abs":"https://arxiv.org/abs/2410.00412v1","url_pdf":"https://arxiv.org/pdf/2410.00412v1.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":"tpn-transferable-proto-learning-network","repo_url":"https://github.com/echodreamer/tpn","is_official":1,"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":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tpn","method_name":"TPN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.00412","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}