  Recent advances in open-domain QA have led to strong models based on dense
retrieval, but only focused on retrieving textual passages. In this work, we
tackle open-domain QA over tables for the first time, and show that retrieval
can be improved by a retriever designed to handle tabular context. We present
an effective pre-training procedure for our retriever and improve retrieval
quality with mined hard negatives. As relevant datasets are missing, we extract
a subset of Natural Questions (Kwiatkowski et al., 2019) into a Table QA
dataset. We find that our retriever improves retrieval results from 72.0 to
81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a
BERT based retriever.
