Learning From Personal Longitudinal Dialog Data

Charles Welch, Verónica Pérez-Rosas, Jonathan K. Kummerfeld, Rada Mihalcea, Erik Cambria

Abstract: We explore the use of longitudinal dialog data for two dialog prediction tasks: next message prediction and response time prediction. We show that a neural model using personal data that leverages a combination of message content, style matching, time features, and speaker attributes leads to the best results for both tasks, with error rate reductions of up to 15% compared to a classifier that relies exclusively on message content and to a classifier that does not use personal data.