  To build Sounding Board, we develop a system architecture that is capable of
accommodating dialog strategies that we designed for socialbot conversations.
The architecture consists of a multi-dimensional language understanding module
for analyzing user utterances, a hierarchical dialog management framework for
dialog context tracking and complex dialog control, and a language generation
process that realizes the response plan and makes adjustments for speech
synthesis. Additionally, we construct a new knowledge base to power the
socialbot by collecting social chat content from a variety of sources. An
important contribution of the system is the synergy between the knowledge base
and the dialog management, i.e., the use of a graph structure to organize the
knowledge base that makes dialog control very efficient in bringing related
content to the discussion. Using the data collected from Sounding Board during
the competition, we carry out in-depth analyses of socialbot conversations and
user ratings which provide valuable insights in evaluation methods for
socialbots. We additionally investigate a new approach for system evaluation
and diagnosis that allows scoring individual dialog segments in the
conversation. Finally, observing that socialbots suffer from the issue of
shallow conversations about topics associated with unstructured data, we study
the problem of enabling extended socialbot conversations grounded on a
document. To bring together machine reading and dialog control techniques, a
graph-based document representation is proposed, together with methods for
automatically constructing the graph. Using the graph-based representation,
dialog control can be carried out by retrieving nodes or moving along edges in
the graph. To illustrate the usage, a mixed-initiative dialog strategy is
designed for socialbot conversations on news articles.
