"The day after the day after tomorrow?" A machine learning approach to adaptive temporal expression generation: training and evaluation with real users

Generating Temporal Expressions (TE) that are easy to understand, unambiguous, and reasonably short is a challenge for humans and Spoken Dialogue Systems. Rather than developing hand-written decision rules, we adopt a data-driven approach by collecting user feedback on a variety of possible TEs in terms of task success, ambiguity, and user preference. The data collected in this work is freely available to the research community. These data were then used to train a simulated user and a reinforcement learning policy that learns an adaptive Temporal Expression generation strategy for a variety of contexts. We evaluate our learned policy both in simulation and with real users and show that this data-driven adaptive policy is a significant improvement over a rule-based adaptive policy, leading to a 24% increase in perceived task completion, while showing a small increase in actual task completion, and a 16% decrease in call duration. This means that dialogues are more efficient and that users are also more confident about the appointment that they have agreed with the system.