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

Synthetic Target Domain Supervision for Open Retrieval QA