@article{nan-etal-2022-fetaqa,
    title = "{F}e{T}a{QA}: Free-form Table Question Answering",
    author = "Nan, Linyong  and
      Hsieh, Chiachun  and
      Mao, Ziming  and
      Lin, Xi Victoria  and
      Verma, Neha  and
      Zhang, Rui  and
      Kry{\'s}ci{\'n}ski, Wojciech  and
      Schoelkopf, Hailey  and
      Kong, Riley  and
      Tang, Xiangru  and
      Mutuma, Mutethia  and
      Rosand, Ben  and
      Trindade, Isabel  and
      Bandaru, Renusree  and
      Cunningham, Jacob  and
      Xiong, Caiming  and
      Radev, Dragomir  and
      Radev, Dragomir",
    editor = "Roark, Brian  and
      Nenkova, Ani",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "10",
    year = "2022",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/2022.tacl-1.3",
    doi = "10.1162/tacl_a_00446",
    pages = "35--49",
    abstract = "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.",
}