metadata

license: cc-by-4.0
task_categories:
  - question-answering
language:
  - en
tags:
  - finance
  - table-text
  - discrete_reasoning
  - numerical_reasoning
size_categories:
  - 10K<n<100K

TAT-QA

TAT-QA (Tabular And Textual dataset for Question Answering) is a large-scale QA dataset, aiming to stimulate progress of QA research over more complex and realistic tabular and textual data, especially those requiring numerical reasoning.

The unique features of TAT-QA include:

The context given is hybrid, comprising a semi-structured table and at least two relevant paragraphs that describe, analyze or complement the table;
The questions are generated by the humans with rich financial knowledge, most are practical;
The answer forms are diverse, including single span, multiple spans and free-form;
To answer the questions, various numerical reasoning capabilities are usually required, including addition (+), subtraction (-), multiplication (x), division (/), counting, comparison, sorting, and their compositions;
In addition to the ground-truth answers, the corresponding derivations and scale are also provided if any.

In total, TAT-QA contains 16,552 questions associated with 2,757 hybrid contexts from real-world financial reports.

For more details, please refer to the project page: https://nextplusplus.github.io/TAT-QA/

Data Format

{
  "table": {                                                            # The tabular data in a hybrid context
    "uid": "3ffd9053-a45d-491c-957a-1b2fa0af0570",                      # The unique id of a table
    "table": [                                                          # The table content which is 2d-array
      [
        "",
        "2019",
        "2018",
        "2017"
      ],
      [
        "Fixed Price",
        "$  1,452.4",
        "$  1,146.2",
        "$  1,036.9"
      ],
      ...
    ]
  },
  "paragraphs": [                                                        # The textual data in a hybrid context comprising at least two associated paragraphs to the table
    {
      "uid": "f4ac7069-10a2-47e9-995c-3903293b3d47",                     # The unique id of a paragraph
      "order": 1,                                                        # The order of the paragraph in all associated paragraphs, starting from 1
      "text": "Sales by Contract Type: Substantially all of              # The content of the paragraph
       our contracts are fixed-price type contracts.
       Sales included in Other contract types represent cost
       plus and time and material type contracts."
    },
    ...
  ],
  "questions": [                                                         # The questions associated to the hybrid context
    {
      "uid": "eb787966-fa02-401f-bfaf-ccabf3828b23",                     # The unique id of a question
      "order": 2,                                                        # The order of the question in all questions, starting from 1
      "question": "What is the change in Other in 2019 from 2018?",      # The question itself
      "answer": -12.6,                                                   # The ground-truth answer
      "derivation": "44.1 - 56.7",                                       # The derivation that can be executed to arrive at the ground-truth answer
      "answer_type": "arithmetic",                                       # The answer type including `span`, `spans`, `arithmetic` and `counting`.
      "answer_from": "table-text",                                       # The source of the answer including `table`, `table` and `table-text`
      "rel_paragraphs": [                                                # The orders of the paragraphs that are relied to infer the answer if any.
        "2"
      ],
      "req_comparison": false,                                           # A flag indicating if `comparison/sorting` is needed to answer the question whose answer is a single span or multiple spans
      "scale": "million"                                                 # The scale of the answer including `None`, `thousand`, `million`, `billion` and `percent`
    }
  ]
}

Citation

@inproceedings{zhu2021tat,
    title = "{TAT}-{QA}: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance",
    author = "Zhu, Fengbin  and
      Lei, Wenqiang  and
      Huang, Youcheng  and
      Wang, Chao  and
      Zhang, Shuo  and
      Lv, Jiancheng  and
      Feng, Fuli  and
      Chua, Tat-Seng",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.254",
    doi = "10.18653/v1/2021.acl-long.254",
    pages = "3277--3287"
}