TSQA: Tabular Scenario Based Question Answering

Authors

  • Xiao Li State Key Laboratory for Novel Software Technology, Nanjing University, China
  • Yawei Sun State Key Laboratory for Novel Software Technology, Nanjing University, China
  • Gong Cheng State Key Laboratory for Novel Software Technology, Nanjing University, China

Keywords:

Question Answering, Education, Neuro-Symbolic AI (NSAI)

Abstract

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.

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Published

2021-05-18

How to Cite

Li, X., Sun, Y., & Cheng, G. (2021). TSQA: Tabular Scenario Based Question Answering. Proceedings of the AAAI Conference on Artificial Intelligence, 35(15), 13297-13305. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17570

Issue

Section

AAAI Technical Track on Speech and Natural Language Processing II