## 4 Discussion  
Table 6 contains an excerpt from an incident-related investigation using the suggested system. The scenario was assessed by adapting the sentences stated by the child in the actual face-to-face investigation transcript. Figure 5 shows a few screens of the corresponding content. A full-scale statement investigation began with a sentence in which the system recalled the entire memory of the incident, according to the invitation question type of the NICHD protocol. First, the system recognized the entity of the incident from the victim's  
Figure 4: Entity recognition performance by the training parameters. statement and, if sufficient entities were not collected, it induced recall once again. This process is shown in lines 1 and 3. When the intent of the statement was classified as an ID, the system stored the recognized entity in the database because it contained the main information of the event. In lines 2 and 4, the system recognized yesterday (WHEN), old man (WHO), touched, and grabbed (ACTION) entities and confirmed that it did not collect information about the WHERE entity. In lines 5-16, the system additionally asked questions about the WHERE entity not mentioned and the WHO, WHEN, ACTION entities mentioned. Based on the type of invitation question, if the intent of the statement was classified as ALM and there was no recognized entity, it was set up to make a statement as much as possible if the child did not know it well. In lines 10-12, the system generated a question to collect additional information about the old man (WHO), but the child's statement was classified as ALM. However, the WHO entity in the sentence was recognized again, and the system judged that additional information about the WHO entity was collected, although the intent was classified as ALM. The question continued about other entities stored in the database. In line 13, the child's statement was classified as SPD. In line 16, as SPD was classified from the previous victim's statement, additional questions were asked about the feelings of the child. Therefore, the proposed system is capable of efficient statement investigation based on a total of three items: (1) From the victim's response sentences collected through NICHD questions that induce free recall of the incident, the main information of the incident can be collected using intent classification and NER. (2) The system can continuously request additional information about the recognized entities and can cope with various unexpected situations such as admitting lack of memory. (3) Scenarios for statement investigations can be set according to the answers of sex crime victims and sentences that deviate from the flow of statement investigations can be controlled. Table 7 shows the performance of the intent classifier with the optimal parameters. The F1-score for the SPD was lower than that for the other intent types. This result was derived from the existence of data that had similar structures, such as an ID, that is, \"He hit me yesterday\", and an SPD, that is, \"I was scared because he hit me yesterday\". As a result, SPD data necessitated a thorough examination. The results verified that KoBERT exhibited a better performance than BERT in classifying sentences with similar structures but different types of intent. Although KoBERT has fewer training parameters and fewer words than BERT, our belief is that advanced tokenizer techniques, such as SentencePiece, enhance its contextual comprehension significantly. Furthermore, in comparing data imbalance levels, SPD shows more minor data than other intent classifiers. This means that the degree of imbalance for each classification class can significantly influence accuracy evaluations. Table 8 shows the performance of the entity recognizer with the optimal parameters. In entity recognition, the KoBERT model showed a higher accuracy than the BERT model under the same augmentation method as the intent classification.