  We present the first end-to-end, transformer-based table question answering
(QA) system that takes natural language questions and massive table corpus as
inputs to retrieve the most relevant tables and locate the correct table cells
to answer the question. Our system, CLTR, extends the current state-of-the-art
QA over tables model to build an end-to-end table QA architecture. This system
has successfully tackled many real-world table QA problems with a simple,
unified pipeline. Our proposed system can also generate a heatmap of candidate
columns and rows over complex tables and allow users to quickly identify the
correct cells to answer questions. In addition, we introduce two new
open-domain benchmarks, E2E_WTQ and E2E_GNQ, consisting of 2,005 natural
language questions over 76,242 tables. The benchmarks are designed to validate
CLTR as well as accommodate future table retrieval and end-to-end table QA
research and experiments. Our experiments demonstrate that our system is the
current state-of-the-art model on the table retrieval task and produces
promising results for end-to-end table QA.
