Enhancing Low-Resource Relation Representations through Multi-View Decoupling
- URL: http://arxiv.org/abs/2312.17267v4
- Date: Thu, 30 May 2024 01:56:51 GMT
- Title: Enhancing Low-Resource Relation Representations through Multi-View Decoupling
- Authors: Chenghao Fan, Wei Wei, Xiaoye Qu, Zhenyi Lu, Wenfeng Xie, Yu Cheng, Dangyang Chen,
- Abstract summary: We propose a novel prompt-based relation representation method, named MVRE.
MVRE decouples each relation into different perspectives to encompass multi-view relation representations.
Our method can achieve state-of-the-art in low-resource settings.
- Score: 21.32064890807893
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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