  Hybrid Question-Answering (HQA), which targets reasoning over tables and
passages linked from table cells, has witnessed significant research in recent
years. A common challenge in HQA and other passage-table QA datasets is that it
is generally unrealistic to iterate over all table rows, columns, and linked
passages to retrieve evidence. Such a challenge made it difficult for previous
studies to show their reasoning ability in retrieving answers. To bridge this
gap, we propose a novel Table-alignment-based Cell-selection and Reasoning
model (TACR) for hybrid text and table QA, evaluated on the HybridQA and
WikiTableQuestions datasets. In evidence retrieval, we design a
table-question-alignment enhanced cell-selection method to retrieve
fine-grained evidence. In answer reasoning, we incorporate a QA module that
treats the row containing selected cells as context. Experimental results over
the HybridQA and WikiTableQuestions (WTQ) datasets show that TACR achieves
state-of-the-art results on cell selection and outperforms fine-grained
evidence retrieval baselines on HybridQA, while achieving competitive
performance on WTQ. We also conducted a detailed analysis to demonstrate that
being able to align questions to tables in the cell-selection stage can result
in important gains from experiments of over 90\% table row and column selection
accuracy, meanwhile also improving output explainability.
