We propose a novel open-domain question answering (ODQA) framework for
answering single/multi-hop questions across heterogeneous knowledge sources.
The key novelty of our method is the introduction of the intermediary modules
into the current retriever-reader pipeline. Unlike previous methods that solely
rely on the retriever for gathering all evidence in isolation, our intermediary
performs a chain of reasoning over the retrieved set. Specifically, our method
links the retrieved evidence with its related global context into graphs and
organizes them into a candidate list of evidence chains. Built upon pretrained
language models, our system achieves competitive performance on two ODQA
datasets, OTT-QA and NQ, against tables and passages from Wikipedia. In
particular, our model substantially outperforms the previous state-of-the-art
on OTT-QA with an exact match score of 47.3 (45 % relative gain).

提出一种新颖的开放领域问答框架，使用中介模块对异构知识源上的单跳 / 多跳问题进行回答。在预训练语言模型的基础上，通过将检索到的证据与其相关的全局上下文链接到图中，并将它们组织成候选证据链，实现了竞争性的性能。在两个 ODQA 数据集 OTT-QA 和 NQ 上，我们的模型显著优于之前最先进的方法，在 OTT-QA 上具有 47.3 的精确匹配分数（相对增益 45%）。