next-tat/TAT-QA

---
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

- [**Project Page**](https://nextplusplus.github.io/TAT-QA/)
- [**Paper - ACL 21**](https://aclanthology.org/2021.acl-long.254/)
- [**Paper - Arxiv**](https://arxiv.org/abs/2105.07624)
- [**Source Code**](https://github.com/NExTplusplus/TAT-QA)
- [**Leaderboard**](https://nextplusplus.github.io/TAT-QA/#leaderboard)

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

```phthon
{
"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

```bash
@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"
}
```