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Chatbot is increasingly thriving in different domains, however, because ofunexpected discourse complexity and training data sparseness, its potentialdistrust hatches vital apprehension. Recently, Machine-Human Chatting Handoff(MHCH), predicting chatbot failure and enabling human-algorithm collaborationto enhance chatbot quality, has attracted increasing attention from industryand academia. In this study, we propose a novel model, Role-Selected SharingNetwork (RSSN), which integrates both dialogue satisfaction estimation andhandoff prediction in one multi-task learning framework. Unlike prior effortsin dialog mining, by utilizing local user satisfaction as a bridge, globalsatisfaction detector and handoff predictor can effectively exchange criticalinformation. Specifically, we decouple the relation and interaction between thetwo tasks by the role information after the shared encoder. Extensiveexperiments on two public datasets demonstrate the effectiveness of our model.

A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis