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.

该研究提出了一种在深度神经匹配网络上利用外部知识进行响应排序的学习框架，实验证明，该方法在包括商业数据在内的三个信息寻求对话数据集上优于各种基线模型，包括多个深度文本匹配模型和多轮对话响应选择的最新方法。