  In open question answering (QA), the answer to a question is produced by
retrieving and then analyzing documents that might contain answers to the
question. Most open QA systems have considered only retrieving information from
unstructured text. Here we consider for the first time open QA over both
tabular and textual data and present a new large-scale dataset Open
Table-and-Text Question Answering (OTT-QA) to evaluate performance on this
task. Most questions in OTT-QA require multi-hop inference across tabular data
and unstructured text, and the evidence required to answer a question can be
distributed in different ways over these two types of input, making evidence
retrieval challenging -- our baseline model using an iterative retriever and
BERT-based reader achieves an exact match score less than 10%. We then propose
two novel techniques to address the challenge of retrieving and aggregating
evidence for OTT-QA. The first technique is to use "early fusion" to group
multiple highly relevant tabular and textual units into a fused block, which
provides more context for the retriever to search for. The second technique is
to use a cross-block reader to model the cross-dependency between multiple
retrieved evidence with global-local sparse attention. Combining these two
techniques improves the score significantly, to above 27%.
