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.
Enhancing Relation Extraction in Low-Resource Scenarios with Multi-View Relation Representation
The recent advancements in prompt-tuning with pre-trained language models (PLMs) have shown great potential for improving relation extraction (RE) tasks. However, in low-resource scenarios where training data is limited, existing prompt-based methods may not perform well due to a shallow understanding of the relation. In this article, we discuss the importance of learning high-quality relation representation in low-resource scenarios for RE and introduce a novel method called MVRE (Multi-View Relation Extraction) that leverages PLMs to enhance performance within the low-resource paradigm.
MVRE tackles the challenge of representing relations in a more comprehensive and accurate manner by decoupling each relation into multiple perspectives. By considering different views or perspectives, MVRE can capture a wider range of relation representations, which ultimately helps improve the accuracy of relation inference. Instead of relying on a single prompt-based representation, MVRE maximizes the likelihood by combining multiple views of the relation during inference.
To further enhance the alignment of multi-view relation-representing virtual words, which contain the semantics of relation labels, MVRE introduces a Global-Local loss and a Dynamic-Initialization method. The Global-Local loss helps optimize the learning process by considering both global and local alignment between the virtual words and relation labels. The Dynamic-Initialization method ensures that the initial representations of the virtual words are specifically tailored to capture the semantics of relation labels during the optimization process.
To evaluate the effectiveness of MVRE, extensive experiments were conducted on three benchmark datasets. The results demonstrate that our method achieves state-of-the-art performance in low-resource settings. This highlights the significance of leveraging PLMs and multi-view representation learning to overcome data scarcity challenges in RE tasks.
Multi-Disciplinary Nature
The concept of MVRE is multi-disciplinary in nature, combining techniques from natural language processing (NLP), machine learning, and deep learning. The utilization of pre-trained language models and the design of a multi-view representation approach require insights from these diverse fields. NLP techniques are used to extract relations from text, machine learning is employed for optimizing the relation inference process, and deep learning helps capture rich semantic representations through PLMs.
Moreover, the Global-Local loss and Dynamic-Initialization methods incorporate principles from optimization theory and information retrieval to enhance the alignment and initialization processes. This interdisciplinary approach allows MVRE to tackle the challenges posed by low-resource scenarios and achieve superior results in RE tasks.
Expert Insight: The MVRE method demonstrates the potential of integrating various disciplines to tackle real-world challenges in natural language processing. By combining techniques from NLP, machine learning, and deep learning, researchers were able to address the limitations of previous prompt-based methods in low-resource scenarios. This opens up new avenues for improving relation extraction tasks and paves the way for further advancements in the field.