We introduce an approach for open-domain question answering (QA) that
retrieves and reads a passage graph, where vertices are passages of text and
edges represent relationships that are derived from an external knowledge base
or co-occurrence in the same article. Our goals are to boost coverage by using
knowledge-guided retrieval to find more relevant passages than text-matching
methods, and to improve accuracy by allowing for better knowledge-guided fusion
of information across related passages. Our graph retrieval method expands a
set of seed keyword-retrieved passages by traversing the graph structure of the
knowledge base. Our reader extends a BERT-based architecture and updates
passage representations by propagating information from related passages and
their relations, instead of reading each passage in isolation. Experiments on
three open-domain QA datasets, WebQuestions, Natural Questions and TriviaQA,
show improved performance over non-graph baselines by 2-11% absolute. Our
approach also matches or exceeds the state-of-the-art in every case, without
using an expensive end-to-end training regime.

本研究提出了一种基于知识库和相关文章文本联合的开放领域问答方法，通过检索和阅读一个包含文本段的图表结构，并采用跨相关段落传播信息的方式更新文本段落表示，从而在 WebQuestions、自然问题和 TriviaQA 三个任务的数据集上实现了 2-11% 的性能提升。