  We developed a machine learning approach that quantifies multiple aspects of
the success or values in Customer Service contacts, at anytime during the
interaction. Specifically, the value/reward function regarding to the
turn-level behaviors across human agents, chatbots and other hybrid dialog
systems is characterized by the incremental information and confidence gain
between sentences, based on the token-level predictions from a multi-task
neural network trained with only weak signals in dialog-level
attributes/states. The resulting model, named Value Profiler, serves as a
goal-oriented dialog manager that enhances conversations by regulating
automated decisions with its reward and state predictions. It supports both
real-time monitoring and scalable offline customer experience evaluation, for
both bot- and human-handled contacts. We show how it improves Amazon customer
service quality in several applications.
