Towards leveraging latent knowledge and Dialogue context for real-world conversational question answering

In many real-world scenarios, the absence of external knowledge source like Wikipedia re-stricts 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.