  Scenario-based question answering (SQA) has attracted an increasing research
interest. Compared with the well-studied machine reading comprehension (MRC),
SQA is a more challenging task: a scenario may contain not only a textual
passage to read but also structured data like tables, i.e., tabular scenario
based question answering (TSQA). AI applications of TSQA such as answering
multiple-choice questions in high-school exams require synthesizing data in
multiple cells and combining tables with texts and domain knowledge to infer
answers. To support the study of this task, we construct GeoTSQA. This dataset
contains 1k real questions contextualized by tabular scenarios in the geography
domain. To solve the task, we extend state-of-the-art MRC methods with TTGen, a
novel table-to-text generator. It generates sentences from variously
synthesized tabular data and feeds the downstream MRC method with the most
useful sentences. Its sentence ranking model fuses the information in the
scenario, question, and domain knowledge. Our approach outperforms a variety of
strong baseline methods on GeoTSQA.
