  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.
