We study the problem of providing recommended responses to customer service agents in live-chat dialogue systems. Smart-reply systems have been widely applied in real-world applications (e.g. Gmail, LinkedIn Messaging), where most of them can successfully recommend reactive responses. However, we observe a major limitation of current methods is that they generally have difficulties in suggesting proactive investigation act (e.g. "Do you perhaps have another account with us?") due to the lack of long-term context information, which indeed act as critical steps for customer service agents to collect information and resolve customers' issues. Thus in this work, we propose an end-to-end method with special focus on suggesting proactive investigative questions to customer agents in Airbnb's customer service live-chat system. Effectiveness of our proposed method can be validated through qualitative and quantitative results.
翻译：我们研究了在现场聊天对话系统中向客户服务代理提供建议答复的问题。智能复读系统被广泛应用于现实世界应用(例如Gmail、LinkedInMessageing),其中多数系统都能够成功地建议反应性回应。然而,我们观察到,目前方法的一个主要局限性是,它们通常难以建议主动调查(例如“你可能与我们有另一个账户吗?” ),因为缺乏长期的背景信息,而这种信息确实成为客户服务代理收集信息和解决客户问题的关键步骤。因此,在这项工作中,我们建议一种端对端方法,特别侧重于向Airbnb的客户服务现场聊天系统中的客户代理提出积极主动的调查问题。我们拟议方法的有效性可以通过定性和定量结果得到验证。
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