Learning From Personal Longitudinal Dialog Data.

IEEE Intelligent Systems(2019)

引用 12|浏览68
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摘要
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 rel...
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关键词
Time factors,Task analysis,Linguistics,Data models,Instant messaging,Microsoft Windows,Feature extraction
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