# AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry

arXiv: https://arxiv.org/abs/2106.12944 · HTML: https://mlsanity.com/paper/2106.12944/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://mlsanity.com/paper/2004.07347/) (2020)
- [FeTaQA: Free-form Table Question Answering](https://mlsanity.com/paper/2104.00369/) (2021)
- [Open Question Answering over Tables and Text](https://mlsanity.com/paper/2010.10439/) (2020)
- [Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision](https://mlsanity.com/paper/1611.00020/) (2016)
- [Open Domain Question Answering over Tables via Dense Retrieval](https://mlsanity.com/paper/2103.12011/) (2021)
- [Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing](https://mlsanity.com/paper/1807.02322/) (2018)
- [Capturing Row and Column Semantics in Transformer Based Question Answering over Tables](https://mlsanity.com/paper/2104.08303/) (2021)
- [Ad Hoc Table Retrieval using Semantic Similarity](https://mlsanity.com/paper/1802.06159/) (2018)
- [TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions](https://mlsanity.com/paper/1602.03960/) (2016)
- [Content-Based Table Retrieval for Web Queries](https://mlsanity.com/paper/1706.02427/) (2017)
- [Scientific Table Search Using Keyword Queries](https://mlsanity.com/paper/1707.03423/) (2017)
- [Schemaless Queries over Document Tables with Dependencies](https://mlsanity.com/paper/1911.09356/) (2019)

## Built on by (most cited first, 30 of 44)

- [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://mlsanity.com/paper/2209.14610/) (2022)
- [Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey](https://mlsanity.com/paper/2402.17944/) (2024)
- [TableBench: A Comprehensive and Complex Benchmark for Table Question Answering](https://mlsanity.com/paper/2408.09174/) (2024)
- [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://mlsanity.com/paper/2206.01347/) (2022)
- [SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation](https://mlsanity.com/paper/2406.14991/) (2024)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers](https://mlsanity.com/paper/2407.09413/) (2024)
- [Multimodal Table Understanding](https://mlsanity.com/paper/2406.08100/) (2024)
- [A Survey on Table Question Answering: Recent Advances](https://mlsanity.com/paper/2207.05270/) (2022)
- [A Survey of Table Reasoning with Large Language Models](https://mlsanity.com/paper/2402.08259/) (2024)
- [Large Language Models are Complex Table Parsers](https://mlsanity.com/paper/2312.11521/) (2023)
- [GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering](https://mlsanity.com/paper/2412.01230/) (2024)
- [TQA-Bench: Evaluating LLMs for Multi-Table Question Answering](https://mlsanity.com/paper/2411.19504/) (2024)
- [TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering](https://mlsanity.com/paper/2506.03949/) (2025)
- [End-to-End Table Question Answering via Retrieval-Augmented Generation](https://mlsanity.com/paper/2203.16714/) (2022)
- [RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis](https://mlsanity.com/paper/2506.13405/) (2025)
- [Parameter-Efficient Abstractive Question Answering over Tables or Text](https://mlsanity.com/paper/2204.03357/) (2022)
- [NeedleInATable: Exploring Long-Context Capability of Large Language Models towards Long-Structured Tables](https://mlsanity.com/paper/2504.06560/) (2025)
- [Granite Embedding R2 Models](https://mlsanity.com/paper/2508.21085/) (2025)
- [Table Question Answering for Low-resourced Indic Languages](https://mlsanity.com/paper/2410.03576/) (2024)
- [General Table Question Answering via Answer-Formula Joint Generation](https://mlsanity.com/paper/2503.12345/) (2025)
- [Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding](https://mlsanity.com/paper/2510.20176/) (2025)
- [Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation](https://mlsanity.com/paper/2602.09305/) (2026)
- [Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework](https://mlsanity.com/paper/2212.10097/) (2022)
- [Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images](https://mlsanity.com/paper/2509.07966/) (2025)
- [TabularMath: Understanding Math Reasoning over Tables with Large Language Models](https://mlsanity.com/paper/2505.19563/) (2025)
- [T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables](https://mlsanity.com/paper/2508.19813/) (2025)
- [TABLET: A Large-Scale Dataset for Robust Visual Table Understanding](https://mlsanity.com/paper/2509.21205/) (2025)
- [Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://mlsanity.com/paper/2510.09671/) (2025)
- [Deep Tabular Research via Continual Experience-Driven Execution](https://mlsanity.com/paper/2603.09151/) (2026)

## Abstract (arXiv)

Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions and WikiSQL. Such transformers are frequently pre-trained on open-domain content such as Wikipedia, where they effectively encode questions and corresponding tables from Wikipedia as seen in Table QA dataset. However, web tables in Wikipedia are notably flat in their layout, with the first row as the sole column header. The layout lends to a relational view of tables where each row is a tuple. Whereas, tables in domain-specific business or scientific documents often have a much more complex layout, including hierarchical row and column headers, in addition to having specialized vocabulary terms from that domain. To address this problem, we introduce the domain-specific Table QA dataset AIT-QA (Airline Industry Table QA). The dataset consists of 515 questions authored by human annotators on 116 tables extracted from public U.S. SEC filings (publicly available at: https://www.sec.gov/edgar.shtml) of major airline companies for the fiscal years 2017-2019. We also provide annotations pertaining to the nature of questions, marking those that require hierarchical headers, domain-specific terminology, and paraphrased forms. Our zero-shot baseline evaluation of three transformer-based SOTA Table QA methods - TaPAS (end-to-end), TaBERT (semantic parsing-based), and RCI (row-column encoding-based) - clearly exposes the limitation of these methods in this practical setting, with the best accuracy at just 51.8% (RCI). We also present pragmatic table preprocessing steps used to pivot and project these complex tables into a layout suitable for the SOTA Table QA models.