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Intelligent personal assistant systems with either text-based or voice-basedconversational interfaces are becoming increasingly popular around the world.Retrieval-based conversation models have the advantages of returning fluent andinformative responses. Most existing studies in this area are on open domain"chit-chat" conversations or task / transaction oriented conversations. Moreresearch is needed for information-seeking conversations. There is also a lackof modeling external knowledge beyond the dialog utterances among currentconversational models. In this paper, we propose a learning framework on thetop of deep neural matching networks that leverages external knowledge forresponse ranking in information-seeking conversation systems. We incorporateexternal knowledge into deep neural models with pseudo-relevance feedback andQA correspondence knowledge distillation. Extensive experiments with threeinformation-seeking conversation data sets including both open benchmarks andcommercial data show that, our methods outperform various baseline methodsincluding several deep text matching models and the state-of-the-art method onresponse selection in multi-turn conversations. We also perform analysis overdifferent response types, model variations and ranking examples. Our models andresearch findings provide new insights on how to utilize external knowledgewith deep neural models for response selection and have implications for thedesign of the next generation of information-seeking conversation systems.

Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems