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Enhancing Low-Resource Relation Representations through Multi-View Decoupling (2312.17267v4)

Published 26 Dec 2023 in cs.CL and cs.AI

Abstract: Recently, prompt-tuning with pre-trained LLMs (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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Authors (7)
  1. Chenghao Fan (7 papers)
  2. Wei Wei (424 papers)
  3. Xiaoye Qu (62 papers)
  4. Zhenyi Lu (9 papers)
  5. Wenfeng Xie (8 papers)
  6. Yu Cheng (354 papers)
  7. Dangyang Chen (20 papers)
Citations (2)
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