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首页> 外文会议>12th Annual meeting of the Special Interest Group on Discourse and Dialogue. >'The day after the day after tomorrow?' A machine learning approach to adaptive temporal expression generation: training and evaluation with real users
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'The day after the day after tomorrow?' A machine learning approach to adaptive temporal expression generation: training and evaluation with real users

机译：“后天的后天？”自适应时间表达生成的机器学习方法：与实际用户的训练和评估

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摘要

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.
机译：生成易于理解，明确且相当短的时间表达（TE）对人类和口语对话系统都是一个挑战。我们没有制定手写的决策规则，而是通过数据驱动的方法来收集有关任务成功，模棱两可和用户偏好的各种可能TE的用户反馈。这项工作中收集的数据可免费提供给研究社区。然后，这些数据被用来训练模拟用户和强化学习策略，该策略学习针对各种环境的自适应时间表达生成策略。我们在仿真中和与实际用户一起评估了我们学到的策略，结果表明，该数据驱动的自适应策略相对于基于规则的自适应策略而言是一项显着的改进，导致感知的任务完成率提高了24％，而在实际任务完成，通话时间减少16％。这意味着对话更加有效，并且用户对他们已同意系统的约会也更有信心。

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