A System for Interviewing and Collecting Statements Based on Intent Classification and Named Entity Recognition Using Augmentation

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.