Journal
IEEE INTELLIGENT SYSTEMS
Volume 34, Issue 4, Pages 16-23Publisher
IEEE COMPUTER SOC
DOI: 10.1109/MIS.2019.2916965
Keywords
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Funding
- Michigan Institute for Data Science
- National Science Foundation [1815291]
- John Templeton Foundation [61156]
- IBM as part of the Sapphire Project
- DARPA [HR001117S0026-AIDA-FP-045]
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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.
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