Leveraging Prompt and Top-K Predictions with ChatGPT Data Augmentation for Improved Relation Extraction

Abstract

Relation extraction tasks aim to predict the type of relationship between two entities from a given text. However, many existing methods fail to fully utilize the semantic information and the probability distribution of the output of pre-trained language models, and existing data augmentation approaches for natural language processing (NLP) may introduce errors. To address this issue, we propose a method that introduces prompt information and Top-K prediction sets and utilizes ChatGPT for data augmentation to improve relational classification model performance. First, we add prompt information before each sample and encode the modified samples by pre-training the language model RoBERTa and using these feature vectors to obtain the Top-K prediction set. We add a multi-attention mechanism to link the Top-K prediction set with the prompt information. We then reduce the possibility of introducing noise by bootstrapping ChatGPT so that it can better perform the data augmentation task and reduce subsequent unnecessary operations. Finally, we investigate the predefined relationship categories in the SemEval 2010 Task 8 dataset and the prediction results of the model and propose an entity location prediction task designed to assist the model in accurately determining the relative locations between entities. Experimental results indicate that our model achieves high results on the SemEval 2010 Task 8 dataset.