  Question answering on tabular data (a.k.a TableQA), which aims at generating
answers to questions grounded on a provided table, has gained significant
attention recently. Prior work primarily produces concise factual responses
through information extraction from individual or limited table cells, lacking
the ability to reason across diverse table cells. Yet, the realm of free-form
TableQA, which demands intricate strategies for selecting relevant table cells
and the sophisticated integration and inference of discrete data fragments,
remains mostly unexplored. To this end, this paper proposes a generalized
three-stage approach: Table-to- Graph conversion and cell localizing, external
knowledge retrieval, and the fusion of table and text (called TAG-QA), to
address the challenge of inferring long free-form answers in generative
TableQA. In particular, TAG-QA (1) locates relevant table cells using a graph
neural network to gather intersecting cells between relevant rows and columns,
(2) leverages external knowledge from Wikipedia, and (3) generates answers by
integrating both tabular data and natural linguistic information. Experiments
showcase the superior capabilities of TAG-QA in generating sentences that are
both faithful and coherent, particularly when compared to several
state-of-the-art baselines. Notably, TAG-QA surpasses the robust pipeline-based
baseline TAPAS by 17% and 14% in terms of BLEU-4 and PARENT F-score,
respectively. Furthermore, TAG-QA outperforms the end-to-end model T5 by 16%
and 12% on BLEU-4 and PARENT F-score, respectively.
