Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-pretty_name: SAF - Legal Domain - German
+pretty_name: SAF - Micro Job - German
 annotations_creators:
 - expert-generated
 language:
@@ -52,6 +52,108 @@ dataset_info:
 ---
 # Dataset Card for "saf_micro_job_german"
 
-# Dataset Summary
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Annotation process](#annotation-process) 
+- [Additional Information](#additional-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Paper:** [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022)
+
+### Dataset Summary
+
+Short Answer Feedback (SAF) is a short answer dataset introduced in [Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset](https://aclanthology.org/2022.acl-long.587) (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 8 German short answers used in micro-job training on the crowd-worker platform appJobber - while the original dataset presented in the paper is comprised of an assortment of both English and German short answer questions (with reference answers). Please refer to the [saf_communication_networks_english](https://huggingface.co/datasets/JohnnyBoy00/saf_communication_networks_english) dataset to examine the English subset of the original dataset.
+
+### Supported Tasks and Leaderboards
+
+- `short_answer_feedback`: The dataset can be used to train a Text2Text Generation model from HuggingFace transformer in order to generate automatic short answer feedback. 
+
+### Languages
+
+The questions, reference answers, provided answers and the answer feedback in the dataset are written in German.
+
+## Dataset Structure
+
+### Data Instances
+
+An example of an entry of the training split looks as follows.
+```
+{
+    "id": "1",
+    "question": "Frage 1: Ist das eine Frage?",
+    "reference_answer": "Ja, das ist eine Frage.",
+    "provided_answer": "Ich bin mir sicher, dass das eine Frage ist.",
+    "answer_feedback": "Korrekt!",
+    "verification_feedback": "Correct",
+    "score": 1
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `id`: a `string` feature (UUID4 in HEX format).
+- `question`: a `string` feature representing a question.
+- `reference_answer`: a `string` feature representing a reference answer to the question.
+- `provided_answer`: a `string` feature representing an answer that was provided for a particular question.
+- `answer_feedback`: a `string` feature representing the feedback given to the provided answers.
+- `verification_feedback`: a `string` feature representing an automatic labeling of the score. It can be `Correct` (`score` = 1), `Incorrect` (`score` = 0) or `Partially correct` (all intermediate scores).
+- `score`: a `float64` feature (between 0 and 1) representing the score given to the provided answer.
+
+### Data Splits
+
+The dataset is comprised of four data splits.
+
+- `train`: used for training, contains a set of questions and the provided answers to them.
+- `validation`: used for validation, contains a set of questions and the provided answers to them (derived from the original training split defined in the paper).
+- `test_unseen_answers`: used for testing, contains unseen answers to the questions present in the `train` split.
+- `test_unseen_questions`: used for testing, contains unseen questions that do not appear in `train` split.
+
+|   name            |train|validation|test_unseen_answers|test_unseen_questions|
+|-------------------|----:|---------:|------------------:|--------------------:|
+|Number of instances| 1226|       308|                271|                  602|
+
+## Dataset Creation
+
+### Annotation Process
+
+Two experienced appJobber employees were selected to evaluate the crowd-worker platform’s answers, and both of them underwent a general annotation guideline training (supervised by a Psychology doctoral student with prior work in the field of feedback). After the training, the annotators individually provided feedback to the answers following an agreed upon scoring rubric and the general annotation guideline. The individually annotated answer files were then combined into a cohesive gold standard after discussing and solving possible disagreements.      
+
+## Additional Information
+
+### Citation Information
+
+```
+@inproceedings{filighera-etal-2022-answer,
+    title = "Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset",
+    author = "Filighera, Anna  and
+      Parihar, Siddharth  and
+      Steuer, Tim  and
+      Meuser, Tobias  and
+      Ochs, Sebastian",
+    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = may,
+    year = "2022",
+    address = "Dublin, Ireland",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.acl-long.587",
+    doi = "10.18653/v1/2022.acl-long.587",
+    pages = "8577--8591",
+}
+```
+
+### Contributions
 
 Thanks to [@JohnnyBoy2103](https://github.com/JohnnyBoy2103) for adding this dataset.