metadata

pretty_name: FaQuAD-NLI
annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - pt
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - extended|wikipedia
task_categories:
  - question-answering
task_ids:
  - extractive-qa
train-eval-index:
  - config: plain_text
    task: question-answering
    task_id: extractive_question_answering
    splits:
      train_split: train
      eval_split: validation
    col_mapping:
      question: question
      context: context
      answers:
        text: text
        answer_start: answer_start
    metrics:
      - type: squad
        name: SQuAD

Dataset Card for FaQuAD-NLI

Dataset Description

Homepage: https://github.com/liafacom/faquad
Repository: https://github.com/liafacom/faquad
Paper: https://ieeexplore.ieee.org/document/8923668/
Point of Contact: Eraldo R. Fernandes eraldoluis@gmail.com

Dataset Summary

FaQuAD is a Portuguese reading comprehension dataset that follows the format of the Stanford Question Answering Dataset (SQuAD). It is a pioneer Portuguese reading comprehension dataset using the challenging format of SQuAD. The dataset aims to address the problem of abundant questions sent by academics whose answers are found in available institutional documents in the Brazilian higher education system. It consists of 900 questions about 249 reading passages taken from 18 official documents of a computer science college from a Brazilian federal university and 21 Wikipedia articles related to the Brazilian higher education system.

FaQuAD-NLI is a modified version of the FaQuAD dataset that repurposes the question answering task as a textual entailment task, where question and answer sentence pairs must be classified as either suitable or unsuitable as an answer.

Supported Tasks and Leaderboards

question_answering: The dataset can be used to train a model for question-answering tasks in the domain of Brazilian higher education institutions.
textual_entailment: FaQuAD-NLI can be used to train a model for textual entailment tasks, where question and answer sentence pairs are classified as either suitable or unsuitable as an answer.

Languages

This dataset is in Brazilian Portuguese.

Dataset Structure

Data Fields

document_index: an integer representing the index of the document.
document_title: a string containing the title of the document.
paragraph_index: an integer representing the index of the paragraph within the document.
question: a string containing the question related to the paragraph.
answer: a string containing the answer related to the question.
label: an integer (0 or 1) representing if the answer is suitable (1) or unsuitable (0) for the question.

Data Splits

The dataset is split into three subsets: train, validation, and test. The splits were made carefully to avoid question and answer pairs belonging to the same document appearing in more than one split.

Train: 3128 instances
Validation: 731 instances
Test: 650 instances

Licensing Information

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Citation Information

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Contributions

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