Learning from Personal Longitudinal Dialog Data


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.

IEEE Intelligent Systems
Jonathan K. Kummerfeld
Jonathan K. Kummerfeld
Tenure-Track Faculty at the University of Sydney (mid-2022)

Postdoc working on Natural Language Processing.