Low Resource Relation Extraction

Low-resource relation extraction (RE) focuses on accurately identifying relationships between entities in text when training data is scarce. Current research emphasizes improving RE performance in such scenarios through techniques like prompt-based learning with pre-trained language models, self-training methods that leverage both confidently and ambiguously labeled data, and novel contrastive learning approaches to better align pre-training and fine-tuning objectives. These advancements are crucial for extending the capabilities of natural language processing to domains with limited annotated resources, impacting applications like knowledge base construction and information extraction.