metadata

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"

Dataset Description
Dataset Structure
Additional Information
- 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 (Filighera et al., ACL 2022). Please refer to saf_micro_job_german and 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 for adding this dataset.

Datasets:

Short-Answer-Feedback
/

saf_legal_domain_german