论文标题
带有对话级弱信号的转向级对话框评估，用于机器人 - 人类混合客户服务系统
Turn-level Dialog Evaluation with Dialog-level Weak Signals for Bot-Human Hybrid Customer Service Systems
论文作者
论文摘要
我们开发了一种机器学习方法，该方法在交互期间的任何时间量化了客户服务联系人的成功或价值的多个方面。具体而言，基于来自令牌神经网络中的多任务神经网络的预测，对对话率属性属性/状态中仅弱信号训练的多任务神经网络的预测，聊天机器人，聊天机器人和其他混合对话系统的价值/奖励功能的特征是句子之间的增量信息和信心增益。最终的模型名为Value Profiler，是面向目标的对话框，该对话管理器通过以其奖励和状态预测来调节自动决策来增强对话。它支持实时监控和可扩展的离线客户体验评估，并为机器人处理的联系人评估。我们展示了它如何在多种应用程序中改善亚马逊客户服务质量。
We developed a machine learning approach that quantifies multiple aspects of the success or values in Customer Service contacts, at anytime during the interaction. Specifically, the value/reward function regarding to the turn-level behaviors across human agents, chatbots and other hybrid dialog systems is characterized by the incremental information and confidence gain between sentences, based on the token-level predictions from a multi-task neural network trained with only weak signals in dialog-level attributes/states. The resulting model, named Value Profiler, serves as a goal-oriented dialog manager that enhances conversations by regulating automated decisions with its reward and state predictions. It supports both real-time monitoring and scalable offline customer experience evaluation, for both bot- and human-handled contacts. We show how it improves Amazon customer service quality in several applications.
