  Goal-oriented conversational agents are becoming prevalent in our daily
lives. For these systems to engage users and achieve their goals, they need to
exhibit appropriate social behavior as well as provide informative replies that
guide users through tasks. The first component of the research in this paper
applies statistical modeling techniques to understand conversations between
users and human agents for customer service. Analyses show that social language
used by human agents is associated with greater users' responsiveness and task
completion. The second component of the research is the construction of a
conversational agent model capable of injecting social language into an agent's
responses while still preserving content. The model uses a sequence-to-sequence
deep learning architecture, extended with a social language understanding
element. Evaluation in terms of content preservation and social language level
using both human judgment and automatic linguistic measures shows that the
model can generate responses that enable agents to address users' issues in a
more socially appropriate way.
