## 2 Related Works  
Relation triplet extraction is an important process in constructing a knowledge graph [1; 2], which mainly involves two entities and the relationship between them. Early studies typically used a pipeline approach [5], which involved two steps. Firstly, named entity recognition was used to extract all entities from the text. Then, relation classification was used to identify the relationships between them. These two steps used two completely independent encoders. However, this extraction method introduces two problems. Firstly, it does not utilize the potential connections between entities and relationships, treating them completely separately. Secondly, there is a cascading error problem, where errors generated during the entity extraction step directly affect the accuracy of the relation classification task. In order to address these challenges, researchers have conducted collaborative studies focusing on joint entity and relation extraction [22; 23]. Early research on joint extraction mainly focused on feature engineering [24], using random discrete variables to represent the results of local entity recognition and relation extraction. However, errors often occur when extracting features using NLP tools, which reduces the overall performance of the model. In recent years, deep neural networks have achieved success in various fields, and researchers have attempted to use a shared encoder for both entity extraction and relation classification in joint learning [22; 23]. Currently, end-to-end [14] extraction methods have demonstrated powerful performance, leading to various branches in the task of joint relational triple extraction. Based on different methods and extraction processes, we categorize the joint extraction task into the following types:  
**Tagging-based methods. ** These approaches typically employ binary labeling sequences to identify the start and end positions of entities, taking into account the inter-relationships between entities across various relationship types. The authors of ref. [8] proposed a cascaded framework called CasRel, which first extracts the subject and then ex tracts the corresponding object based on the subject for relationship classification prediction. In ref. [25], the authors decomposed the joint extraction of triplets into three sub-tasks and used a relationship prioritization method to filter redundant relationships, leading to significant improvements in computational efficiency and extraction accuracy compared to the CasRel model. Despite the good performance achieved by these methods, they involve multiple sub-modules or steps, which leads to exposure bias and insufficient batching capability. Additionally, the integration of relationship information into the sentence to assist entity recognition tasks has not been well applied. **Generation-based methods. ** These approaches generally utilize RNN models as a foundation and adopt the Seq2 Seq [26] framework to transform the extraction task into a generation task, generating triplets in a fixed format. In ref. [27], the authors proposed an end-to-end language generation model called REBEL for relation extraction. This model is flexible in adapting to new domains and longer document data without the need to train specific modules from scratch, making the training process more efficient. However, this method struggles with handling long-distance dependency issues and also considers the context when generating triplets, leading to higher computational efficiency and model complexity. **Table-filling-based methods. ** These approaches transform the extraction task into a table-filling task, where each relationship is represented by a table, and each item in the table indicates whether the corresponding token pair in the row and column has that specific relationship. Some models use the diagonal as the vector space for NER tasks, such as those in refs. [28; 29], but such labeling schemes struggle with nested entities and complex sentence structures.