解决少样本文档关系抽取中跨域泛化差的问题，提升模型对未知关系的识别能力。
TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction
- 设计可插拔的原型学习模块，自适应构建未知关系原型，缓解跨域偏差。
- 在FREDo和ReFREDo数据集上优于主流方法，参数量仅为一半。
- 适合需要小样本、跨领域关系抽取的工业级应用，如知识图谱构建。
少样本文档级关系抽取因未知关系（NOTA）表示的跨域迁移能力差而表现不佳。本文提出可迁移的原型学习网络（TPN），包含三个核心组件：混合编码器通过融合语义与注意力信息增强关系表示；可迁移原型学习器作为跨域检测的即插即用模块，利用可学习块计算NOTA原型，有效缓解不同领域间的NOTA偏差；动态加权校准器检测特定关系分类置信度，作为动态权重调节以抑制主导性的NOTA损失函数。此外，为提升跨域性能，引入虚拟对抗训练（VAT）。在FREDo和ReFREDo数据集上进行广泛实验，结果表明TPN优于现有最先进方法，在参数量约为其一半的情况下仍保持竞争力。代码与数据见https://github.com/EchoDreamer/TPN。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。