## 2 Related Work  
The current mainstream data augmentation methods are paraphrase based, mainly based on making appropriate changes to a sentence to generate enhanced data with few semantic differences from the original data. These enhanced data convey information very similar to the original form. Mainstream interpretation-based approaches include synonym replacement, semantic embedding, machine translation, model generation, and so on [13]. Synonym replacement consists of obtaining all replaceable words for each sentence and randomly replacing n phrases. The more similar a new word is to the original word, the more likely it is to be replaced [14; 15]. This approach is very convenient, but the scope of the enhanced words is limited. Failure to properly replace a word will also affect the semantics of the corresponding sentence. Semantic embedding methods use pre-trained word embedding, such as Glove, Word2 Vec, FastText, etc. [16], thus overcoming the problems that dictionary-based methods have with replacement ranges and parts of speech. However, since the corresponding model does not consider context information, it is still prone to precipitating ambiguity after replacement. Also, with the development of machine translation models and the availability of online APIs, machine translation has become a popular augmentation method for many tasks. This method is easy to use and guarantees correct syntax and invariant semantics but is poorly controllable and has limited diversity due to the involvement of fixed machine translation models [13]. Due to their excellent performance, pre-trained language models have become the mainstream models for data augmentation tasks in recent years. Masked language models such as BERT and Roberta can predict the masked words in text based on the context [17], which can then be used for text data expansion. This approach considers context semantics and alleviates ambiguity. However, the data augmentation process is still limited to the words. If there are too many replacement words, the semantics of sentences will also be affected. For electronic medical records, masking the critical features of negative samples may impact the original text labels. Therefore, in some approaches, the Seq2 Seq model is used to generate text without masking words directly. [PERSON] et al. (2020) used a transformer as the basic structure [18]. They used masked original sentences and their label sequences to train the corresponding model, which reconstructed masked fragments into enhanced data. [PERSON] et al. (2020) used a pre-trained model to provide prior information to the proposed transformer-based model to generate context-sensitive answerable and unanswerable questions [19]. As shown in Table 1, we compared the advantages and limitations of each data augmentation method. SimBERT is a semantic similarity model based on BERT that can generate data by being trained using a large-scale corpus [20]. Microsoft transformed BERT into a model that can handle the task of natural language generation, namely, the UniLM model [21]. Using UniLM, we can complete text generation, which is the premise behind using SimBERT to generate text. Currently, the SimBERT model is widely used for text data augmentation tasks. [PERSON] et al. (2021) use SimBERT to produce a synonymous sentence for those new questions after entity and relation replacement to guarantee question corpus diversity. They generate ten synonymous sentences for each new question using SimBERT and randomly select one from those sentences to be the final version of the question [22]. [PERSON] et al. (2023) generated a few samples using the SimBERT model and mixed them with the original samples to obtain a distributed balanced dataset. Then, the mixed dataset is inputted into a deep-learning model to complete the emotion classification task. The experimental results show that this method has good classification performance [23]. The generated model has a broader application range and can directly enhance data without masking words.