Open Domain Question Answering requires systems to retrieve external
knowledge and perform multi-hop reasoning by composing knowledge spread over
multiple sentences. In the recently introduced open domain question answering
challenge datasets, QASC and OpenBookQA, we need to perform retrieval of facts
and compose facts to correctly answer questions. In our work, we learn a
semantic knowledge ranking model to re-rank knowledge retrieved through Lucene
based information retrieval systems. We further propose a "knowledge fusion
model" which leverages knowledge in BERT-based language models with externally
retrieved knowledge and improves the knowledge understanding of the BERT-based
language models. On both OpenBookQA and QASC datasets, the knowledge fusion
model with semantically re-ranked knowledge outperforms previous attempts.

本文提出一种基于语义知识排名模型和知识融合模型的开放领域问答方法，该方法通过整合基于 Lucene 的信息检索系统检索的知识和 BERT 语言模型中的外部检索知识，以正确回答问题。研究表明，基于知识融合模型的方法优于之前的尝试。