Learning From Personal Longitudinal Dialog Data

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

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.