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Relation Classification via Bidirectional Prompt Learning with Data Augmentation by Large Language Model

The Relation Extraction (RE) task aims to extract the relation between two entities in a sentence. As the performance of methods on RE task depends on datasets’ quantity and quality, in this paper, we propose to use the Large Language Model (LLM) to do data augmentation. Moreover, compared to traditional fine-tuning methods, more research focuses on prompt learning. However, all of their prompt templates ignore the relative order of entities, which we believe will affect the prediction error. Due to that, we propose novel bidirectional prompt templates for prompt learning and design a training strategy for utilizing the templates. Then we try to fit the probability distributions of both prompt learning and fine-tuning methods into our model. To this end, we propose Relation Classification via Bidirectional Prompt learning with data augmentation by LLM (RCBP) and conduct experiments on four datasets: TACRED, RETACRED, TACREV and Semeval. The results show that RCBP performs well on these datasets and outperforms the state-of-the-art in the TACREV, RETACRED datasets.