## 2 Related Work  
In the past, ABSA was carried out separately from two subtasks, only studying ATE [10], aiming to extract aspects in the text, or only studying ASC [11], using the aspect items obtained in advance to classify their sentiment, both of which currently have good results, but they cannot see the full picture and actual effect of complete ABSA. There is a pipeline method [12], which first uses one model to extract the aspects contained in the text and then another model to classify the sentiment of these aspects and complete two subtasks simultaneously to obtain a complete ABSA model. The two subtmodels of this approach may work well when used separately, but this pipe-based approach requires data to flow across multiple models that cannot be backpropagated, and the erroneous predictions of one model are passed on to the next, so the end result must be wrong. In addition, the pipeline method requires training two models separately, which also results in a waste of computing resources. Therefore, end-to-end research is necessary.  
To process two subtasks at the same time, [PERSON] et al. [5] proposed a multi-task neural learning framework that can simultaneously process ATE and ASC subtasks and use the attention mechanism to learn the joint representation of aspect and affective relationship. [PERSON] et al. [6] used stacked LSTMs to predict the boundary and unified prediction of aspect terms and designed three auxiliary components to correct the prediction results. These end-to-end models can achieve multiple tasks in one step, avoid the conduction of false predictions in multiple models, and at the same time, can make the error backpropagate within the model, ultimately reducing the error rate and improving the accuracy rate. However, they only complete the two subtasks at the same time without considering the connection between them, so the effect is also poor. [PERSON] et al. [13] and [PERSON] et al. [14] proposed that the previous research did not make full use of the interconnection of the two subtasks and proposed a shared network and a multi-task learning network, respectively,using different methods to achieve the interaction between the two subtasks. However, the study of the two of them only linked the two subtasks and did not expand on other content, so the results were more general.  
With the continuous development of deep learning, the pre-trained model BERT [15] gradually replaced the traditional word vector model and was widely used in various research fields of natural language processing. [PERSON] et al. [16] first applied BERT to end-to-end ABSA when they constructed a series of simple but effective neural baselines for sequence labeling problems and used BERT to fine-tune task-specific components. The results showed that the pre-trained model BERT was very effective; just BERT plus a simple linear classification layer had better results than before.  
[PERSON] et al. [17] proposed that the imbalance between labels would affect performance, so they extended the gradient coordination strategy to alleviate this problem and used virtual adversarial training and post-training of domain datasets to improve collaborative extraction performance. They came to the conclusion that alleviating the label imbalance problem is more important for sequence labeling. [PERSON] et al. [18] proposed a deep-context relationship-aware network DCRAN based on aspect sentiment analysis, which allows implicit interaction between subtasks in a more efficient way and allows two explicit self-monitoring strategies for deep context and relationship-aware learning; the results show that their two explicit self-monitoring strategies are very effective.  
The above research improves the performance of the model by exploring different contents but does not solve the problem of the connection between the aspects and maintaining the sentiment consistency within aspect items. Hence, an interactive learning network that maintains sentiment consistency is proposed.