Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
<|reference_start|>Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features: Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relationship between two entities of interest appearing in a free-text sentence. Conventional approaches on RC, regardless of feature engineering or deep learning based, can obtain promising performance on categorizing common types of relation leaving a large proportion of unrecognizable long-tail relations due to insufficient labeled instances for training. In this paper, we consider few-shot learning is of great practical significance to RC and thus improve a modern framework of metric learning for few-shot RC. Specifically, we adopt the large-margin ProtoNet with fine-grained features, expecting they can generalize well on long-tail relations. Extensive experiments were conducted by FewRel, a large-scale supervised few-shot RC dataset, to evaluate our framework: LM-ProtoNet (FGF). The results demonstrate that it can achieve substantial improvements over many baseline approaches.<|reference_end|>
@article{fan2024large,
    title={Large Margin Prototypical Network for Few-shot Relation Classification
  with Fine-grained Features},
    author={Miao Fan, Yeqi Bai, Mingming Sun, Ping Li},
    journal={arXiv preprint arXiv:2409.04009},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04009},
    primaryClass={cs.CL}
}