Is it possible to predict task completion in automated troubleshooters?
Is it possible to predict task completion in automated troubleshooters?
复制标题
是否可以在自动故障排除程序中预测任务完成情况？
DOI：
10.21437/interspeech.2010-42
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
D. Suendermann
中科院分区：
文献类型：
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作者：
Alexander Schmitt;Michael Scholz;W. Minker;J. Liscombe;D. Suendermann
交互式语音应答（IVR）系统中任务成功的在线预测是一个比较新的研究领域。它有助于识别有问题的呼叫，并使对话系统能够在呼叫者过度沮丧之前做出反应。本出版物调查了在多大程度上可以预测任务完成情况，以及现有方法如何适用于长对话。我们比较了两种不同建模技术的性能：线性建模和n-gram建模。我们表明，在以后的预测点，n-gram建模明显优于线性建模。从一组综合的交互参数中，我们使用信息增益比识别相关参数。提出并评价了新的交互参数。这项研究基于来自自动互联网故障排除器的41,422个电话，平均每个电话21.4次。
Thede online prediction of task success in Interactive Voice Response (IVR) systems is a comparatively new field of research. It helps to identify problemantic calls and enables the dialog system to react before the caller gets overly frustrated. This publication investigates, to which extent it is possible to predict task completion and how existing approaches generalize for long dialogs. We compare the performance of two different modeling techniques: linear modeling and n-gram modeling. We show that n-gram modeling outperforms linear modeling significantly at later prediction points. From a comprehensive set of interaction parameters, we identify the relevant ones using the Information Gain Ratio. New interaction parameters are presented and evaluated. The study is based on 41,422 calls from an automated Internet troubleshooter with an average of 21.4 turns per call.