Prompt-Contrastive Learning for Zero-Shot Relation Extraction.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Relation extraction is crucial for knowledge acquisition but requires extensive data annotation.
• Zero-shot learning addresses annotation costs by enabling models to identify unseen relations.
• Current zero-shot methods struggle with diverse task formulations, leading to suboptimal performance.

Purpose of the Study:

• To develop a novel approach for zero-shot relation extraction by exploiting knowledge from pre-trained language models.
• To improve the semantic representation capabilities of relation extraction models for unseen relations.
• To introduce a Prompt-Contrastive learning framework (PCRE) for enhanced zero-shot relation extraction.

Main Methods:

• Leveraging semantic knowledge from pre-trained language models via prompt tuning.
• Augmenting instances with diverse prompt templates to create dual views for contrastive learning.
• Implementing an instance-description contrastive objective to extract relational knowledge from text descriptions.

Main Results:

• The proposed PCRE method significantly outperforms existing state-of-the-art baselines in zero-shot relation extraction.
• Experimental results demonstrate the robustness of PCRE across various datasets and training configurations.
• PCRE effectively enhances the model's ability to differentiate between seen and unseen relations.

Conclusions:

• PCRE offers a promising direction for improving zero-shot relation extraction by effectively utilizing pre-trained language models.
• The prompt-contrastive learning strategy enhances semantic representations, leading to superior performance in identifying novel relations.
• The method's robustness suggests its broad applicability in real-world knowledge extraction tasks.