FeTaQA: Free-form Table Question Answering

By Linyong Nan, Chia-Hsuan Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryscinski, Nick Schoelkopf, Riley Kong, Xiangru Tang and 7 more. 2021

274 citations recorded by the source corpus.

Existing table question answering datasets contain abundant factual questions that primarily evaluate a QA system’s comprehension of query and tabular data. However, restricted by their short-form answers, these datasets fail to include question–answer interactions that represent more advanced and naturally occurring information needs: questions that ask for reasoning and integration of information pieces retrieved from a structured knowledge source. To complement the existing datasets and to reveal the challenging nature of the table-based question answering task, we introduce FeTaQA, a new dataset with 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs. FeTaQA is collected from noteworthy descriptions of Wikipedia tables that contain information people tend to seek; generation of these descriptions requires advanced processing that humans perform on a daily basis: Understand the question and table, retrieve, integrate, infer, and conduct text planning and surface realization to generate an answer. We provide two benchmark methods for the proposed task: a pipeline method based on semantic parsing-based QA systems and an end-to-end method based on large pretrained text generation models, and show that FeTaQA poses a challenge for both methods.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

UL2: Unifying Language Learning Paradigms (2022, methodology): “, 2022), namely WikiTQ (Pasupat & Liang, 2015), CompWQ (Talmor & Berant, 2018), FetaQA (Nan et al., 2021), HybridQA (Chen et al.”
Unifying Language Learning Paradigms (2022, methodology): “, 2022), namely WikiTQ (Pasupat & Liang, 2015), CompWQ (Talmor & Berant, 2018), FetaQA (Nan et al., 2021), HybridQA (Chen et al.”
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning (2022, background): “Recent work aims at introducing datasets that require multi-hop reasoning between the textual and tabular data: Hy-bridQA (Chen et al., 2020b), OTTQA (Chen et al., 2020a), MultiModalQA (Talmor et al., 2020), AIT-QA (Katsis et al., 2021), and FeTaQA (Nan et al., 2022).”
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation (2021, background): “While QG/QA modeling per se is out of the scope of this paper, further improvements could be obtained thanks to advances in multimodal QA, a field particularly prolific (Chen et al., 2020; Herzig et al., 2020; Nan et al., 2021).”
Constrained Dominant Sets for Multimodal Document Question Answering (2026, unknown): “These modalities include slides (SlideVQA [33]), scientific figures and tables (SPIQA [34], PaperTab [35] and FetaTab [36]), charts (SciGraphQA [37]), and multi-document settings (VisDoMBench [4]).”
Min-Cost Flow Routing for Evidence Assembly in Long Multimodal Documents (2026, unknown): “Relevant benchmarks include VisDoMBench (Suri et al., 2025), SlideVQA (Tanaka et al., 2023), SPIQA (Pramanick et al., 2024), and UDA (Hui et al., 2024), which incorporates FeTaQA (Nan et al., 2022) and QASPER (Dasigi et al., 2021).”