metadata

license: apache-2.0
task_categories:
  - question-answering
  - text-generation
language:
  - it
tags:
  - question-answering
  - question-generation
  - education
  - children education
size_categories:
  - 10K<n<100K

Dataset Card for FairytaleQA-translated-ptBR

Dataset Description

Homepage:
Repository: https://github.com/bernardoleite/fairytaleqa-translated
Paper: https://arxiv.org/abs/2406.04233v1
Leaderboard: https://paperswithcode.com/sota/question-generation-on-fairytaleqa
Point of Contact: Bernardo Leite (benjleite.com)

Dataset Summary

This repository contains the Italian machine-translated version of the original English FairytaleQA dataset (https://huggingface.co/datasets/WorkInTheDark/FairytaleQA). FairytaleQA is an open-source dataset designed to enhance comprehension of narratives, aimed at students from kindergarten to eighth grade. The dataset is meticulously annotated by education experts following an evidence-based theoretical framework. It comprises 10,580 explicit and implicit questions derived from 278 child-friendly stories, covering seven types of narrative elements or relations. This translation was performed using DeepL as part of our research: FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages.

You can load the dataset via:

import datasets
data = datasets.load_dataset('benjleite/FairytaleQA-translated-italian')

Supported Tasks and Leaderboards

Question-Answering, Question-Generation, Question-Answer Pair Generation

Languages

Italian

Example

An example of "train" looks as follows:

{
  'story_name': 'the-toad-woman-story',
  'story_section': 'Una volta capitò una grande fortuna a una giovane donna che viveva da sola nel bosco,...'
  'question': 'Chi vide la donna scivolare via nella foresta?',
  'answer': 'Un bel giovane.',
  'local-or-sum': 'local',
  'attribute': 'character',
  'ex-or-im': 'explicit',
  'ex-or-im2': '',
}

Dataset Structure

story_name*: a string of the story name to which the story section content belongs.
story_section: a string of the story section(s) content related to the experts' labeled QA-pair. Used as the input for both Question Generation and Question Answering tasks.
question: a string of the question content. Used as the input for Question Answering task and as the output for Question Generation task.
answer: a string of the answer content for all splits. Used as the input for Question Generation task and as the output for Question Answering task.
local_or_sum*: a string of either local or summary, indicating whether the QA is related to one story section or multiple sections.
attribute*: a string of one of character, causal relationship, action, setting, feeling, prediction, or outcome resolution. Classification of the QA by education experts annotators via 7 narrative elements on an established framework.
ex_or_im1*: a string of either explicit or implicit, indicating whether the answers can be directly found in the story content or cannot be directly from the story content.
ex_or_im2*: similar to 'ex-or-im1', but annotated by another annotator (only available for test/val splits).

(*) Field has not been translated. Use it at your own convenince.

Data Splits

The split sizes are as follows:

	Train	Validation	Test
# Books	232	23	23
# QA-Pairs	8548	1025	1007

Additional Information

Licensing Information

This dataset version is released under the Apache-2.0 License (as the original dataset).

Citation Information

Our paper (preprint - accepted for publication at ECTEL 2024):

@article{leite_fairytaleqa_translated_2024,
        title={FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages}, 
        author={Bernardo Leite and Tomás Freitas Osório and Henrique Lopes Cardoso},
        year={2024},
        eprint={2406.04233},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
}

Original FairytaleQA paper:

@inproceedings{xu-etal-2022-fantastic,
    title = "Fantastic Questions and Where to Find Them: {F}airytale{QA} {--} An Authentic Dataset for Narrative Comprehension",
    author = "Xu, Ying  and
      Wang, Dakuo  and
      Yu, Mo  and
      Ritchie, Daniel  and
      Yao, Bingsheng  and
      Wu, Tongshuang  and
      Zhang, Zheng  and
      Li, Toby  and
      Bradford, Nora  and
      Sun, Branda  and
      Hoang, Tran  and
      Sang, Yisi  and
      Hou, Yufang  and
      Ma, Xiaojuan  and
      Yang, Diyi  and
      Peng, Nanyun  and
      Yu, Zhou  and
      Warschauer, Mark",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    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.34",
    doi = "10.18653/v1/2022.acl-long.34",
    pages = "447--460",
    abstract = "Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. Drawing on the reading education research, we introduce FairytaleQA, a dataset focusing on narrative comprehension of kindergarten to eighth-grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. Our dataset is valuable in two folds: First, we ran existing QA models on our dataset and confirmed that this annotation helps assess models{'} fine-grained learning skills. Second, the dataset supports question generation (QG) task in the education domain. Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.",
}

Contact

Bernardo Leite (bernardo.leite@fe.up.pt)