Answering questions that require multi-hop reasoning at web-scale
necessitates retrieving multiple evidence documents, one of which often has
little lexical or semantic relationship to the question. This paper introduces
a new graph-based recurrent retrieval approach that learns to retrieve
reasoning paths over the Wikipedia graph to answer multi-hop open-domain
questions. Our retriever model trains a recurrent neural network that learns to
sequentially retrieve evidence paragraphs in the reasoning path by conditioning
on the previously retrieved documents. Our reader model ranks the reasoning
paths and extracts the answer span included in the best reasoning path.
Experimental results show state-of-the-art results in three open-domain QA
datasets, showcasing the effectiveness and robustness of our method. Notably,
our method achieves significant improvement in HotpotQA, outperforming the
previous best model by more than 14 points.

介绍了一种基于图形的循环检索方法，学习在 Wikipedia 图形上检索推理路径来回答多跳开放域问答问题。该方法在三个开放域 QA 数据集上实现了最新技术成果，特别是在 HotpotQA 中表现出显著的改进，超过了以前最好的模型 14 个百分点以上。