|
--- |
|
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" |
|
} |
|
``` |
