Neural passage retrieval is a new and promising approach in open retrieval
question answering. In this work, we stress-test the Dense Passage Retriever
(DPR) -- a state-of-the-art (SOTA) open domain neural retrieval model -- on
closed and specialized target domains such as COVID-19, and find that it lags
behind standard BM25 in this important real-world setting. To make DPR more
robust under domain shift, we explore its fine-tuning with synthetic training
examples, which we generate from unlabeled target domain text using a
text-to-text generator. In our experiments, this noisy but fully automated
target domain supervision gives DPR a sizable advantage over BM25 in
out-of-domain settings, making it a more viable model in practice. Finally, an
ensemble of BM25 and our improved DPR model yields the best results, further
pushing the SOTA for open retrieval QA on multiple out-of-domain test sets.

本研究针对新的神经通道检索方法加以探讨，发现在 COVID-19 的封闭和专业目标域中，SOTA 模型 Dense Passage Retriever 比标准 BM25 差距较大。通过用文本生成器来生成合成训练示例对其进行微调，使其在对外域数据处理中更具鲁棒性，效果有所提高。最终，BM25 与改进的 DPR 模型的集成产生了最佳结果，进一步推动了开放式检索 QA 的 SOTA，适用于多个出域测试集。