Chatbot has become an important solution to rapidly increasing customer care demands on social media in recent years. However, current work on chatbot for customer care ignores a key to impact user experience - tones. In this work, we create a novel tone-aware chatbot that generates toned responses to user requests on social media. We first conduct a formative research, in which the effects of tones are studied. Significant and various influences of different tones on user experience are uncovered in the study. With the knowledge of effects of tones, we design a deep learning based chatbot that takes tone information into account. We train our system on over 1.5 million real customer care conversations collected from Twitter. The evaluation reveals that our tone-aware chatbot generates as appropriate responses to user requests as human agents. More importantly, our chatbot is perceived to be even more empathetic than human agents.
翻译：查波特已成为近年来迅速增加社交媒体对客户护理需求的重要解决方案。 然而,当前为客户护理而开展的工作忽略了影响用户经验的关键。 在这项工作中,我们创建了新型的有声调的聊天博特,对用户在社交媒体上的要求产生有声调的反应。我们首先进行成型研究,研究通的效应。研究中发现了不同调子对用户经验的重大影响和各种影响。根据对通恩影响的了解,我们设计了一个深层次的基于学习的聊天博特,将语气信息考虑在内。我们用从Twitter收集的超过150万次真正的客户护理对话对我们的系统进行了培训。评估显示,我们有声调的聊天博特对用户作为人类代理人的要求产生了适当的反应。更重要的是,我们的聊天博特被认为比人类代理人更具有同情心。
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