In many real-world scenarios, the absence of external knowledge source like
Wikipedia restricts question answering systems to rely on latent internal
knowledge in limited dialogue data. In addition, humans often seek answers by
asking several questions for more comprehensive information. As the dialog
becomes more extensive, machines are challenged to refer to previous
conversation rounds to answer questions. In this work, we propose to leverage
latent knowledge in existing conversation logs via a neural Retrieval-Reading
system, enhanced with a TFIDF-based text summarizer refining lengthy
conversational history to alleviate the long context issue. Our experiments
show that our Retrieval-Reading system can exploit retrieved background
knowledge to generate significantly better answers. The results also indicate
that our context summarizer significantly helps both the retriever and the
reader by introducing more concise and less noisy contextual information.

该论文提出了一种基于神经检索 - 阅读 (Retrieval-Reading) 系统和基于 TFIDF 的文本摘要技术来利用先前的谈话历史中的潜在知识的方法，以便更好地回答问题，并且实验证明该系统显著提高了回答质量。