Datasets:

Short-Answer-Feedback
/

saf_legal_domain_german

	---
	pretty_name: SAF - Legal Domain - German
	annotations_creators:
	- expert-generated
	language:
	- de
	language_creators:
	- other
	multilinguality:
	- monolingual
	size_categories:
	- 1K<n<10K
	source_datasets:
	- original
	tags:
	- short answer feedback
	- legal domain
	task_categories:
	- text2text-generation
	dataset_info:
	features:
	- name: id
	dtype: string
	- name: question
	dtype: string
	- name: reference_answer
	dtype: string
	- name: provided_answer
	dtype: string
	- name: answer_feedback
	dtype: string
	- name: verification_feedback
	dtype: string
	- name: error_class
	dtype: string
	- name: score
	dtype: float64
	splits:
	- name: train
	num_bytes: 2142112
	num_examples: 1596
	- name: validation
	num_bytes: 550206
	num_examples: 400
	- name: test_unseen_answers
	num_bytes: 301087
	num_examples: 221
	- name: test_unseen_questions
	num_bytes: 360616
	num_examples: 275
	download_size: 484808
	dataset_size: 3354021
	license: cc-by-4.0
	---

	# Dataset Card for "saf_legal_domain_german"

	## 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)
	- [Additional Information](#additional-information)
	- [Contributions](#contributions)

	## Dataset Description

	### Dataset Summary

	This Short Answer Feedback (SAF) dataset contains 19 German questions in the domain of the German social law (with reference answers). The idea of constructing a bilingual (English and German) short answer dataset as a way to remedy the lack of content-focused feedback datasets was 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). Please refer to [saf_micro_job_german](https://huggingface.co/datasets/Short-Answer-Feedback/saf_micro_job_german) and [saf_communication_networks_english](https://huggingface.co/datasets/Short-Answer-Feedback/saf_communication_networks_english) for similarly constructed datasets that can be used for SAF tasks.

	### Supported Tasks and Leaderboards

	- `short_answer_feedback`: The dataset can be used to train a Text2Text Generation model from HuggingFace transformers 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": "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",
	"error_class": "Keine",
	"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).
	- `error_class`: a `string` feature representing the type of error identified in the case of a not completely correct answer.
	- `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 set from which the data came from).
	- `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 the `train` split.

	\| Split \|train\|validation\|test_unseen_answers\|test_unseen_questions\|
	\|-------------------\|----:\|---------:\|------------------:\|--------------------:\|
	\|Number of instances\| 1596\| 400\| 221\| 275\|

	## Additional Information

	### Contributions
	Thanks to [@JohnnyBoy2103](https://github.com/JohnnyBoy2103) for adding this dataset.