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Question answering on tabular data (a.k.a TableQA), which aims at generatinganswers to questions grounded on a provided table, has gained significantattention recently. Prior work primarily produces concise factual responsesthrough information extraction from individual or limited table cells, lackingthe ability to reason across diverse table cells. Yet, the realm of free-formTableQA, which demands intricate strategies for selecting relevant table cellsand the sophisticated integration and inference of discrete data fragments,remains mostly unexplored. To this end, this paper proposes a generalizedthree-stage approach: Table-to- Graph conversion and cell localizing, externalknowledge retrieval, and the fusion of table and text (called TAG-QA), toaddress the challenge of inferring long free-form answers in generativeTableQA. In particular, TAG-QA (1) locates relevant table cells using a graphneural network to gather intersecting cells between relevant rows and columns,(2) leverages external knowledge from Wikipedia, and (3) generates answers byintegrating both tabular data and natural linguistic information. Experimentsshowcase the superior capabilities of TAG-QA in generating sentences that areboth faithful and coherent, particularly when compared to severalstate-of-the-art baselines. Notably, TAG-QA surpasses the robust pipeline-basedbaseline 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.

Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tables