Update README.md

README.md

@@ -1,11 +1,111 @@
----
-license: cc-by-4.0
-task_categories:
-- question-answering
-language:
-- en
-tags:
-- finance
-size_categories:
-- 10K<n<100K
----
+---
+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/);  [**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"
+}
+```