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TSQA: Tabular Scenario Based Question Answering

arXiv 2101.11429 ↗First posted 14 Jan 2021cs.CL cs.AIcited by 21

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Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 21 of its 38 references that are in the map. First posted on arXiv 14 Jan 2021.

Builds on

21 of 38 references.

References · 21

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9 more
  1. 2019Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehensioncited by 80
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  9. 2019GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Levelcited by 17

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Citing papers per year: 2021: 1; 2022: 8; 2023: 4; 2024: 5; 2025: 3.

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  1. 2022MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Datacited by 120
  2. 2023A Survey of Reasoning with Foundation Modelscited by 70
  3. 2022A Survey on Table Question Answering: Recent Advancescited by 41
  4. 2024TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Datacited by 19
  5. 2022Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decodercited by 17
  6. 2024TQA-Bench: Evaluating LLMs for Multi-Table Question Answeringcited by 17
  7. 2023VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Modelscited by 15
  8. 2024See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknessescited by 15
  9. 2022DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Datacited by 10
  10. 2022Clues Before Answers: Generation-Enhanced Multiple-Choice QAcited by 9
  11. 2022AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehensioncited by 8
  12. 2024Exploring Hybrid Question Answering via Program-based Promptingcited by 8
9 more
  1. 2022A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directionscited by 7
  2. 2023Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tablescited by 6
  3. 2022NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answeringcited by 4
  4. 2023Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approachcited by 4
  5. 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 4
  6. 2021When Retriever-Reader Meets Scenario-Based Multiple-Choice Questionscited by 3
  7. 2025Synthetic Clarification and Correction Dialogues about Data-Centric Tasks -- A Teacher-Student Approachcited by 3
  8. 2024FormulaReasoning: A Dataset for Formula-Based Numerical Reasoningcited by 2
  9. 2025MULTITAT: Benchmarking Multilingual Table-and-Text Question Answeringcited by 2