Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks. However, in low-resource scenarios, where the available training data is scarce, previous prompt-based methods may still perform poorly for prompt-based representation learning due to a superficial understanding of the relation. To this end, we highlight the importance of learning high-quality relation representation in low-resource scenarios for RE, and propose a novel prompt-based relation representation method, named MVRE (\underline{M}ulti-\underline{V}iew \underline{R}elation \underline{E}xtraction), to better leverage the capacity of PLMs to improve the performance of RE within the low-resource prompt-tuning paradigm. Specifically, MVRE decouples each relation into different perspectives to encompass multi-view relation representations for maximizing the likelihood during relation inference. Furthermore, we also design a Global-Local loss and a Dynamic-Initialization method for better alignment of the multi-view relation-representing virtual words, containing the semantics of relation labels during the optimization learning process and initialization. Extensive experiments on three benchmark datasets show that our method can achieve state-of-the-art in low-resource settings.

翻译：近期，基于预训练语言模型（PLMs）的提示调优方法在关系抽取（RE）任务中展现出显著的性能提升能力。然而，在训练数据稀缺的低资源场景下，由于对关系的表层理解不足，现有基于提示的方法在提示基表示学习中仍可能表现不佳。为此，我们强调在低资源场景下为关系抽取学习高质量关系表示的重要性，并提出一种新型基于提示的关系表示方法MVRE（多视角关系抽取），以更充分地利用PLMs的能力，在低资源提示调优范式下提升关系抽取性能。具体而言，MVRE将每个关系解耦为不同视角以包含多视角关系表示，从而在关系推理过程中最大化似然概率。此外，我们还设计了全局-局部损失函数和动态初始化方法，用于更好对齐包含关系标签语义的多视角关系表示虚拟词，优化学习过程与初始化阶段。在三个基准数据集上的大量实验表明，我们的方法在低资源场景下能够取得最先进性能。