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.

中文翻译：

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一种用于人机联合聊天切换和服务满意度分析的角色选择共享网络

聊天机器人在不同领域日益繁荣，然而，由于意外的话语复杂性和训练数据的稀疏性，其潜在的不信任引发了至关重要的担忧。最近，机器人聊天切换（MHCH），预测聊天机器人失败并实现人与算法协作以提高聊天机器人质量，越来越受到工业界和学术界的关注。在这项研究中，我们提出了一种新模型，角色选择共享网络（RSSN），它在一个多任务学习框架中集成了对话满意度估计和切换预测。与先前在对话挖掘中的努力不同，通过利用本地用户满意度作为桥梁，全局满意度检测器和切换预测器可以有效地交换关键信息。具体来说，我们通过共享编码器之后的角色信息来解耦两个任务之间的关系和交互。对两个公共数据集的大量实验证明了我们模型的有效性。