  Most existing end-to-end Table Question Answering (Table QA) models consist
of a two-stage framework with a retriever to select relevant table candidates
from a corpus and a reader to locate the correct answers from table candidates.
Even though the accuracy of the reader models is significantly improved with
the recent transformer-based approaches, the overall performance of such
frameworks still suffers from the poor accuracy of using traditional
information retrieval techniques as retrievers. To alleviate this problem, we
introduce T-RAG, an end-to-end Table QA model, where a non-parametric dense
vector index is fine-tuned jointly with BART, a parametric sequence-to-sequence
model to generate answer tokens. Given any natural language question, T-RAG
utilizes a unified pipeline to automatically search through a table corpus to
directly locate the correct answer from the table cells. We apply T-RAG to
recent open-domain Table QA benchmarks and demonstrate that the fine-tuned
T-RAG model is able to achieve state-of-the-art performance in both the
end-to-end Table QA and the table retrieval tasks.
