Tingting Hang , Wei Wu , Jun Feng , Hamza Djigal , Jun Huang
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A survey of Few-Shot Relation Extraction combining meta-learning with prompt learning
Few-shot Relation Extraction (FSRE) is challenging in Natural Language Processing (NLP). It requires models to accurately identify and categorize the relation between entities in texts based on limited annotated samples. Researchers have explored meta-learning and prompt-learning methods to address this challenge. Meta-learning aims to accelerate the model’s learning rate for new tasks by training across different tasks, while prompt learning seeks to enhance the model’s understanding of context. However, both methods face limitations in few-shot scenarios. To overcome these restrictions, studies combining meta-learning with prompt learning have emerged, garnering widespread academic interest. This paper presents a comprehensive survey of FSRE combining Meta-Learning with Prompt Learning. We first categorize existing research into three main groups: meta-learning-based, prompt learning-based, and hybrid approaches. Then, we provide a detailed analysis and discussion of the latest advancements in each category. Moreover, we extensively describe datasets commonly used in the FSRE task, evaluation metrics, and comparisons among various models. Finally, we explore current challenges in the field and forecast potential future research trends, offering insights for subsequent studies.
期刊介绍:
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.