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.

翻译：然而,由于突发性谈话的复杂性和培训数据稀少,查波特在不同领域日益兴旺发达,因此,由于潜在的不信任,其潜在的不信任感引发了重要的忧虑。 最近,机器-人类聊天交接(MHCH),预测聊天机器人失败,并促成人与人之间的交流,以提高聊天机器人的质量,这引起了产业界和学术界越来越多的关注。在这项研究中,我们提出了一个新颖的模式,即角色选择共享网络(RSSN),将对话满意度估计和搭接预测都纳入一个多任务学习框架中。与先前的对话采矿工作不同,利用当地用户满意度作为桥梁、全球满意度探测器和手动预测器可以有效地交换关键信息。具体地说,我们分解了共享编码器之后角色信息在两种任务间的关系和互动。关于两个公共数据集的广泛实验显示了我们模式的有效性。