TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction
<|reference_start|>TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction: 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.<|reference_end|>
@article{zhang2024tpn:,
    title={TPN: Transferable Proto-Learning Network towards Few-shot Document-Level
  Relation Extraction},
    author={Yu Zhang and Zhao Kang},
    journal={arXiv preprint arXiv:2410.00412},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00412},
    primaryClass={cs.CL cs.IR}
}