角色选择共享网络的人机联合聊天系统及服务满意度分析
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis
摘要：
聊天机器人在各个领域日益蓬勃发展，但由于其话语的复杂性和训练数据的稀疏性，其潜在的不信任性引发了人们的担忧。最近，人机聊天机器人（MHCH），预测聊天机器人失败，使人类算法合作，以提高聊天机器人的质量，引起了越来越多的关注，从工业界和学术界。在这项研究中，我们提出了一种新的模型，角色选择共享网络（RSSN），它集成了对话满意度估计和切换预测在一个多任务学习框架。与以往的对话挖掘方法不同，该方法利用局部用户满意度作为桥梁，实现了全局满意度检测器和切换预测器之间的有效交互。具体地说，我们通过共享编码器后的角色信息来解耦两个任务之间的关系和交互。在两个公共数据集上的实验证明了该模型的有效性。
英文摘要：
Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension. Recently, Machine-Human Chatting Handoff (MHCH), predicting chatbot failure and enabling human-algorithm collaboration to enhance chatbot quality, has attracted increasing attention from industry and academia. In this study, we propose a novel model, Role-Selected Sharing Network (RSSN), which integrates both dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. Unlike prior efforts in dialog mining, by utilizing local user satisfaction as a bridge, global satisfaction detector and handoff predictor can effectively exchange critical information. Specifically, we decouple the relation and interaction between the two tasks by the role information after the shared encoder. Extensive experiments on two public datasets demonstrate the effectiveness of our model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
