  Intelligent personal assistant systems with either text-based or voice-based
conversational interfaces are becoming increasingly popular around the world.
Retrieval-based conversation models have the advantages of returning fluent and
informative responses. Most existing studies in this area are on open domain
"chit-chat" conversations or task / transaction oriented conversations. More
research is needed for information-seeking conversations. There is also a lack
of modeling external knowledge beyond the dialog utterances among current
conversational models. In this paper, we propose a learning framework on the
top of deep neural matching networks that leverages external knowledge for
response ranking in information-seeking conversation systems. We incorporate
external knowledge into deep neural models with pseudo-relevance feedback and
QA correspondence knowledge distillation. Extensive experiments with three
information-seeking conversation data sets including both open benchmarks and
commercial data show that, our methods outperform various baseline methods
including several deep text matching models and the state-of-the-art method on
response selection in multi-turn conversations. We also perform analysis over
different response types, model variations and ranking examples. Our models and
research findings provide new insights on how to utilize external knowledge
with deep neural models for response selection and have implications for the
design of the next generation of information-seeking conversation systems.
