Measuring Conversational Productivity in Child Forensic Interviews
This work addresses the high-stakes problem of improving information retrieval and reducing trauma for legal interviewers in child forensic settings, though it is incremental as a first step toward broader modeling.
The paper tackled the challenge of measuring conversational productivity in child forensic interviews by proposing a novel computational methodology using summarization and topic modeling to objectively rank responsiveness and information retrieval, achieving alignment with traditional evaluation metrics.
Child Forensic Interviewing (FI) presents a challenge for effective information retrieval and decision making. The high stakes associated with the process demand that expert legal interviewers are able to effectively establish a channel of communication and elicit substantive knowledge from the child-client while minimizing potential for experiencing trauma. As a first step toward computationally modeling and producing quality spoken interviewing strategies and a generalized understanding of interview dynamics, we propose a novel methodology to computationally model effectiveness criteria, by applying summarization and topic modeling techniques to objectively measure and rank the responsiveness and conversational productivity of a child during FI. We score information retrieval by constructing an agenda to represent general topics of interest and measuring alignment with a given response and leveraging lexical entrainment for responsiveness. For comparison, we present our methods along with traditional metrics of evaluation and discuss the use of prior information for generating situational awareness.