## 2 Related Work
Subsequently, based on TPLinker and CasRel, [PERSON] et al. proposed a triple joint extraction framework called PRGC, which is based on latent relationships and global communication. However, this method still needs to improve the generalization ability of overlapping entities. Additionally, the structure of PRGC is complex and requires high training processes [24]. [PERSON] et al. proposed a global normalization approach called GlobalPointer for identifying both nested and non-nested entities [25]. The approach involves constructing an entity matrix with a tagging method similar to TPLinker and training the model by incorporating a new multi-label cross-entropy loss function. Based on GlobalPointer, researchers proposed a joint entity-relation extraction model called GPLinker, which utilizes multiple GlobalPointers for extracting entities and relationships [26]. Regular multiple pointer networks have been applied to recognize multiple entity relationship categories. Each pointer network introduced two modules to identify the head and tail of the entity [19; 20]. During training and evaluation, a single module can only consider the information of either the entity's head or tail, which results in inconsistencies in taking the global information of the entity into account during training and prediction processes. However, the global pointer network treats the head and tail positions of the entity simultaneously, which can supply the global perspective for the model. So, consistency in training and prediction goals can be achieved, and the performance of the entity relationship extraction model can be enhanced [27]. TPLinker also introduced the multi-label cross-entropy loss function from GPLinker and TPLinker-plus to solve category imbalance problems [23]. However, the discussed models often require entity annotations for each predefined relation in the set, which leads to redundant relation judgments. In addition, the multi-task problem also remained during the joint extraction of entities and relations. Multi-task learning (MTL) is a powerful machine learning algorithm that leverages the inherent relationships between multiple related tasks to enhance overall model performance [28]. Further, MTL can be valuable in overcoming the challenges associated with data scarcity, which can alleviate model overfitting and significantly enhance model performance. MTL generally employs parameter sharing for learning, which can pro vide data augmentation in scenarios for multiple tasks that share commonalities. Such approaches can enhance the model's performance compared to training separate models on individual datasets [29]. With a focus on language processing, MTL is widely employed, especially for the main tasks of entity relation extraction and relation filtering. The varying importance and data distributions of different tasks often lead to imbalance when directly summing the loss functions from all tasks in most models. So, more research has been focused on strategies and optimization methods for adjusting loss function weights in MTL to improve model performance and training effectiveness. The advanced methods include Gradient Normalization (GradNorm), Dynamic Weight Averaging (DWA), Dynamic Task Prioritization (DTP), and Uncertainty to Weigh Losses (UWL). GradNorm was defined to equalize the gradient magnitude of different tasks, aiming to ensure similar learning speeds across tasks [30]. DWA, like GradNorm, aimed to balance learning speeds among tasks but focused on adjusting weights based on the change ratio of each task's loss to assess its difficulty. If tasks with a faster decrease in loss were easier to learn, the higher weights assigned to tasks presented slower learning speeds [31]. DTP introduced the concept of task priority by categorizing tasks based on difficulty, adaptively adjusting weights, and assigning higher weights to harder tasks during each training session [32]. UWL introduced noise as a measure of task difficulty, assigning higher weights to tasks with lower noise levels and higher certainty [33].