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data/test_unseen_questions-00000-of-00001-3c502ac25141f64c.parquet → JohnnyBoy00--saf_micro_job_german/parquet-test_unseen_answers.parquet

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data/validation-00000-of-00001-0da65f8dcc3502f2.parquet → JohnnyBoy00--saf_micro_job_german/parquet-test_unseen_questions.parquet

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data/train-00000-of-00001-d7d79904ef6ab584.parquet → JohnnyBoy00--saf_micro_job_german/parquet-train.parquet

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data/test_unseen_answers-00000-of-00001-1eac04e462a6f413.parquet → JohnnyBoy00--saf_micro_job_german/parquet-validation.parquet

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README.md

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----
-pretty_name: SAF - Micro Job - German
-annotations_creators:
-- expert-generated
-language:
-- de
-language_creators:
-- other
-multilinguality:
-- monolingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-tags:
-- short answer feedback
-- micro job
-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: score
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 885526
-    num_examples: 1226
-  - name: validation
-    num_bytes: 217946
-    num_examples: 308
-  - name: test_unseen_answers
-    num_bytes: 198832
-    num_examples: 271
-  - name: test_unseen_questions
-    num_bytes: 545524
-    num_examples: 602
-  download_size: 274603
-  dataset_size: 1847828
----
-# Dataset Card for "saf_micro_job_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)
-- [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) dataset 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 questions 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. Furthermore, a similarly constructed SAF dataset (covering the German legal domain) can be found at [saf_legal_domain_german](https://huggingface.co/datasets/JohnnyBoy00/saf_legal_domain_german). 
-
-### 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": "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 set 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 the `train` split.
-
-|   Split           |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.

conversion.py

@@ -1,96 +0,0 @@
-import os
-import string
-import math
-import random
-import xml.etree.ElementTree as et
-import jsonlines
-import uuid
-
-# set random seed for shuffling
-random.seed(1)
-
-def convert_xml_to_jsonl(path_to_dataset, dir, filename, train_split=None):
-    """
-    Utility function used for conversion of XML files from the dataset into JSON lines
-    
-    Params:
-        path_to_dataset (string): path to the folder containing the dataset (in XML format)
-        dir (string): name of the directory where the JSON lines file will be stored
-        filename (string): name of the JSON lines file that will store the dataset
-        train_split (float or None): if not None, defines which percentage of the dataset to use for the train and validation splits  
-    
-    Returns:
-        None: the file is saved in JSON lines format in the specified location
-    """
-    data = []
-
-    # loop through all files in directory
-    for f in os.listdir(path_to_dataset):
-        if f.endswith('.xml'):
-            root = et.parse(os.path.join(path_to_dataset, f)).getroot()
-            # get question
-            question = root.find('questionText').text.replace('\n', ' ')
-            # get reference and student answers
-            ref_answers = [x for x in root.find('referenceAnswers')]
-            student_answers = [x for x in root.find('studentAnswers')]
-
-            if len(ref_answers) == 1:
-                # get reference answer and clear all spaces
-                ref_answer = ref_answers[0].text.strip()
-
-                # loop through all student answers and store the appropriate fields in a list
-                for answer in student_answers:
-                    response = answer.find('response').text.strip()
-                    score = float(answer.find('score').text)
-                    feedback = answer.find('response_feedback').text.strip()
-                    verification_feedback = answer.find('verification_feedback').text.strip()
-
-                    # create dictionary with the appropriate fields
-                    data.append({
-                        'id': uuid.uuid4().hex, # generate unique id in HEX format
-                        'question': question,
-                        'reference_answer': ref_answer,
-                        'provided_answer': response,
-                        'answer_feedback': feedback,
-                        'verification_feedback': verification_feedback,
-                        'score': score
-                    })
-
-    if not os.path.exists(dir):
-        print('Creating directory where JSON file will be stored\n')
-        os.makedirs(dir)
-    
-    if train_split is None:
-        with jsonlines.open(f'{os.path.join(dir, filename)}.jsonl', 'w') as writer:
-            writer.write_all(data)
-    else:
-        # shuffle data and divide it into train and validation splits
-        random.shuffle(data)
-        train_data = data[: int(train_split * (len(data) - 1))]
-        val_data = data[int(train_split * (len(data) - 1)) :]
-
-        # write JSON lines file with train data
-        with jsonlines.open(f'{os.path.join(dir, filename)}-train.jsonl', 'w') as writer:
-            writer.write_all(train_data)
-        
-        # write JSON lines file with validation data
-        with jsonlines.open(f'{os.path.join(dir, filename)}-validation.jsonl', 'w') as writer:
-            writer.write_all(val_data)
-
-if __name__ == '__main__':
-    # convert micro job dataset (german) to JSON lines
-    convert_xml_to_jsonl(
-        'data/training/german',
-        'data/json',
-        'saf-micro-job-german',
-        train_split=0.8)
-
-    convert_xml_to_jsonl(
-        'data/unseen_answers/german',
-        'data/json',
-        'saf-micro-job-german-unseen-answers')
-
-    convert_xml_to_jsonl(
-        'data/unseen_questions/german',
-        'data/json',
-        'saf-micro-job-german-unseen-questions')