Existing approaches for open-domain question answering (QA) are typically
designed for questions that require either single-hop or multi-hop reasoning,
which make strong assumptions of the complexity of questions to be answered.
Also, multi-step document retrieval often incurs higher number of relevant but
non-supporting documents, which dampens the downstream noise-sensitive reader
module for answer extraction. To address these challenges, we propose a unified
QA framework to answer any-hop open-domain questions, which iteratively
retrieves, reranks and filters documents, and adaptively determines when to
stop the retrieval process. To improve the retrieval accuracy, we propose a
graph-based reranking model that perform multi-document interaction as the core
of our iterative reranking framework. Our method consistently achieves
performance comparable to or better than the state-of-the-art on both
single-hop and multi-hop open-domain QA datasets, including Natural Questions
Open, SQuAD Open, and HotpotQA.

本文提出了一种统一的 QA 框架，以回答任何跨域问题，包括多步骤文档检索，图形重新排序模型及多文档交互等技术，从而提高检索精度及性能。