## 2 Related Work  
Relation extraction is a crucial aspect of the NLP domain, aiming to determine the relationship between two entities in a given sentence. The performance of traditional relation extraction models depends on the quality of extracted features. However, feature extraction using NLP tools often introduces noise. An example is the ambiguity of word meanings. Consider a sentence: \"I bought an apple\". In this context, \"apple\" could refer to a fruit or a technology company, and this ambiguity is a potential source of noise, which can degrade model performance. To reduce the noise introduced during feature extraction, in recent years, several models have emerged that use deep neural networks for supervised relation extraction tasks, which are capable of making predictions about the type of relationship between specified entities in text. Ref. [6] introduced a model that employs Convolutional Neural Networks (CNNs) to extract lexical and sentence-level features. In this model, they initially convert each word into a vector through word embedding. Then, lexical-level features are extracted based on the given noun. Simultaneously, a CNN is employed to extract the sentence-level features. Finally, the two levels of features are fused into a final feature vector and fed into a SoftMax layer to predict the relationship between the two entities. Nevertheless, due to CNN's limitations, this model may require assistance in accurately predicting relationship types between entities, particularly in long sentences with entities positioned far apart. Graph Convolutional Networks (GCNs) [7] are a widely used structure in which the information of each node in each GCN layer communicates with neighboring nodes through edges between them. The ability of GCNs to efficiently capture semantic relationships and contextual information between entities in a text has been demonstrated by many previous studies [8; 9; 10]. Many models use dependency trees to build graphs. However, the graphs generated by this method can be noisy, particularly when generated automatically. Noise may be present in the generated graphs because the algorithms constructing the dependency trees cannot handle complex syntactic structures or the text is ambiguous. Excessive reliance on dependency trees may harm the performance of relational extraction tasks. In recent years, pre-trained language models have gained significant attention in various research areas for their potent semantic representations. BERT belongs to the Transformer architecture family and stands out for its ability to perform bidirectional context modeling. Unlike traditional models, BERT considers both a word's left and right context, providing a more comprehensive understanding of language context. Trained unsupervised on large-scale unlabeled text data, BERT learns universal language representations, making it a versatile tool for various NLP tasks. The model's impact extends to tasks such as text classification, named entity recognition, and relation extraction, showcasing its effectiveness through fine-tuning. RoBERTa builds upon the foundation of BERT. Unlike BERT, RoBERTa removes the bidirectional training restriction and employs larger text corpora for pretraining, enhancing its language representation capabilities. Notable improvements include the introduction of dynamic masking, where the mask length is adjusted dynamically during each training iteration, facilitating better contextual learning. [PERSON]. [11] proposed the R-BERT model, which uses BERT to extract relational features and fuses the information of head and tail entities to accomplish the relational extraction task, leading to a notable enhancement in model performance. Using BERT for the relationship classification task inevitably brings a gap between pre-training and fine-tuning for traditional data pre-processing, impacting the model's performance [12]. To address this issue, a new fine-tuning paradigm, Prompt tuning, is proposed based on pre-trained language models.