In recent years, conversational agents have provided a natural and convenient
access to useful information in people's daily life, along with a broad and new
research topic, conversational question answering (QA). Among the popular
conversational QA tasks, conversational open-domain QA, which requires to
retrieve relevant passages from the Web to extract exact answers, is more
practical but less studied. The main challenge is how to well capture and fully
explore the historical context in conversation to facilitate effective
large-scale retrieval. The current work mainly utilizes history questions to
refine the current question or to enhance its representation, yet the relations
between history answers and the current answer in a conversation, which is also
critical to the task, are totally neglected. To address this problem, we
propose a novel graph-guided retrieval method to model the relations among
answers across conversation turns. In particular, it utilizes a passage graph
derived from the hyperlink-connected passages that contains history answers and
potential current answers, to retrieve more relevant passages for subsequent
answer extraction. Moreover, in order to collect more complementary information
in the historical context, we also propose to incorporate the multi-round
relevance feedback technique to explore the impact of the retrieval context on
current question understanding. Experimental results on the public dataset
verify the effectiveness of our proposed method. Notably, the F1 score is
improved by 5% and 11% with predicted history answers and true history answers,
respectively.

本文提出了一种基于图模型和关联反馈的检索方法，用于连续的问答交互中提高检索效率和准确性。实验结果显示，与当前流行的方法相比，该方法大幅提高了问题回答的 F1 得分。