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Recently, a simple combination of passage retrieval using off-the-shelf IRtechniques and a BERT reader was found to be very effective for questionanswering directly on Wikipedia, yielding a large improvement over the previousstate of the art on a standard benchmark dataset. In this paper, we present adata augmentation technique using distant supervision that exploits positive aswell as negative examples. We apply a stage-wise approach to fine tuning BERTon multiple datasets, starting with data that is "furthest" from the test dataand ending with the "closest". Experimental results show large gains ineffectiveness over previous approaches on English QA datasets, and we establishnew baselines on two recent Chinese QA datasets.

Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering