  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.
