## 2 Materials and Methods
### Data Augmentation  
Data augmentation is a method proposed to increase the accuracy of deep learning model training by increasing the volume of data required for such training. [PERSON] et al. [28] demonstrated that models applying simple text augmentation techniques practically exhibited substantial performance in sentence classification tasks. They proposed data augmentation methods using internal data instead of language model and external data. These methods included synonym replacement (SR), which replaced a certain word in a sentence with a synonym; random insertion (RI), which inserted a random word; random swap (RS), which swapped the location of two random words in a sentence; and random deletion (RD), which deleted a random word. In these easy data augmentation (EDA) methods, to convert the number (\(n\)) of words to be adjusted according to the technique applied, the length (\(l\)) of the original sentence was multiplied with a parameter (\(\alpha\)) that had a value ranging from \"0\" to \"1\" (\(n=\alpha l\)). Accordingly, studies have been conducted to increase the performance of classification models by applying these EDA methods. [PERSON] et al. [29] proposed an opinion classification model according to government health systems, which applied an EDA method to solve the problem related to an insufficient amount of label data for model training. [PERSON] et al. [30] adjusted an NER task and EDA method according to projects in the biomedical and materials science domains, and compared the derived datasets based on the adjusted methods with existing datasets. Based on the comparison results, they verified that the adjusted methods exhibited better performance levels than conventional methods. The intent classifier and entity recognizer were trained using face-to-face survey statements as data. A total of 102 statements were obtained from the Supreme Prosecutor's Office, encompassing crimes such as rape, attempted rape, indecent acts by compulsion, pseudo-sexual acts, and other offenses falling under the \"sexual assault act\", \"act on the protection of children\", \"juveniles from sexual abuse\", and \"criminal act\". The dataset was developed based on statements that were deemed reliable by the testimony analyst, who synthesized the investigation atmosphere, tone of the victim, expression method, psychological state, and internal-external information to determine the intent to be classified and entity to be recognized. Among the intent classifications, ALM, ID, and SPD comprised 19%, 64%, and 17% of the total premise intent classifications, respectively. However, this distribution was imbalanced and required balancing for more accurate intent classification. Similarly, the recognition of entity names exhibited significant imbalances across categories, ranging from 1% to 40%. Given the limited amount of data obtained from the statement, there was a concern about overfitting during model training, potentially leading to reduced classification and recognition performance in practical applications. Recognizing the critical importance of these models in legal proceedings, data augmentation techniques were employed to mitigate these issues, and enhance the performance of intent classification and namedentity recognition. These efforts were essential to ensure the reliability and effectiveness of the suggested models in court decisions. We searched for and applied various techniques to solve the low resource data problem and improve classification and recognition performance by diversifying the expressions of sentences and words in the learning data. Hence, few-shot learning was adopted to improve classification performance in a low-resource environment and vocabulary replacement using a pre-trained language model. However, the vocabulary used by children during the face-to-face investigation of child sex crimes was used only in special situations.