Question answering on tabular data (TableQA), which aims at generating
answers to questions grounded on a given table, has attracted increasing
attention in recent years. Existing work tends to generate factual short-form
answers by extracting information from one or a few table cells without
reasoning over selected table cells. However, the free-form TableQA, requiring
a more complex relevant table cell selection strategy and the complex
integration and inference of separate pieces of information, has been
under-explored. To this end, this paper proposes a generalized three-stage
approach: Table-to-Graph conversion and cell localizing, external knowledge
retrieval and table-text fusion (called TAG-QA), addressing the challenge of
inferring long free-form answer for 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 with a human evaluation
demonstrate that TAG-QA is capable of generating more faithful and coherent
sentence when compared with several state-of-the-art baselines. Especially,
TAG-QA outperforms the strong pipeline-based baseline TAPAS by 17% and 14%, in
terms of BLEU-4 and PARENT F-score, respectively. Moreover, TAG-QA outperforms
end-to-end model T5 by 16% and 12% on BLEU-4 and PARENT F-score.

该论文提出了一种名为 TAG-QA 的广义三阶段方法，通过表格到图的转换、外部知识检索和表格 - 文本融合来解决生成型 TableQA 中推断长篇自由格式答案的挑战。实验显示，TAG-QA 在 BLEU-4 和 PARENT F-score 指标上优于多个最先进的基准模型。