###### Abstract  
In cases of child sexual abuse, interviewing and obtaining trustworthy statements from victims and witnesses is essential because their statements are the only evidence. It is crucial to ascertain objectively the credibility of the victim's statements, which may vary based on the nature of the questions posed by the forensic interviewer. Therefore, interview skills that eliminate subjective opinions require a high level of training for forensic interviewers. To reduce high-risk subjective interviews, objectively analyzing statements is essential. Understanding the victim's intent and named entity recognition (NER) in the statements is necessary to give the victim open-ended questions and memory recall. Therefore, the system provides an intent classification and NER method that follows the National Institute of Child Health and Human Development Investigative Interview Protocol, which outlines the collection of objective statements. Large language models such as BERT and KoBERT, along with data augmentation techniques, were proposed using a restricted training dataset of limited size to achieve effective intent classification and NER performance. Additionally, a system that can collect objective statements with the proposed model was developed and it was confirmed that it could assist statement analysts. The verification results showed that the model achieved average F1-scores of 95.5% and 97.8% for intent classification and NER, respectively, which improved the results of the limited data by 3.4% and 3.7%, respectively. child sexual abuse interview; intent classification; named entity recognition; data augmentation; NICHD +
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However, even if investigators are trained to follow the NICHD protocol, most field studies tend to rely heavily on closed-ended questions influenced by subjective opinions. This means that, after listening to statements, investigators ask leading questions based on their suppositions or ask questions about intent based on psychological factor. These problems have been described in many studies in the literature [4; 5] and emphasize the need for highly trained investigators to conduct interviews with open-ended questions [6; 7]. Furthermore, investigators often conduct repeated interviews with children when they believe that insufficient information was obtained in the initial interview. In cases where victim statements are considered critical evidence (e.g., child sexual abuse and sexual harassment), investigators are encouraged to minimize the number of interviews. Repeated interviews might cause secondary victimization as the interviewees are forced to recall negative memories repeatedly. Research on child sexual abuse victims indicates that children have difficulty recounting the full details of encounters, owing to trauma, excessive self-criticism, and guilt, because they often blame themselves for what happened. Thus, a face-to-face interview with a stranger could burden child victims. Therefore, obtaining reliable statements from child sexual abuse victims requires a system that (1) follows the principles of the NICHD interview protocol, (2) minimizes any physical or psychological adverse effects, and (3) provides a child-friendly interview environment. [PERSON] et al. [8] said that artificial intelligence and cognitive interviewing could be essential in eliciting quality evidentiary statements from victims and witnesses. This paper proposes a system that has the function of assisting in obtaining trustworthy statements based on NICHD. This requires accurate intent classification and NER from statements. This study investigated and validated methods for intent classification and NER to establish a foundation for a system that adheres to the NICHD interview rules for child sexual assault. This study demonstrates that colloquial language can be recognized in a particular environment by augmenting limited data and applying intent classification and NER. Our primary contributions are summarized as follows.