metadata

annotations_creators:
  - no-annotation
language_creators:
  - expert-generated
language:
  - en
  - es
license:
  - mit
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - multiple-choice-qa
paperswithcode_id: headqa
pretty_name: HEAD-QA
dataset_info:
  - config_name: es
    features:
      - name: name
        dtype: string
      - name: year
        dtype: string
      - name: category
        dtype: string
      - name: qid
        dtype: int32
      - name: qtext
        dtype: string
      - name: ra
        dtype: int32
      - name: image
        dtype: image
      - name: answers
        list:
          - name: aid
            dtype: int32
          - name: atext
            dtype: string
    splits:
      - name: train
        num_bytes: 1229678
        num_examples: 2657
      - name: test
        num_bytes: 1204006
        num_examples: 2742
      - name: validation
        num_bytes: 573354
        num_examples: 1366
    download_size: 79365502
    dataset_size: 3007038
  - config_name: en
    features:
      - name: name
        dtype: string
      - name: year
        dtype: string
      - name: category
        dtype: string
      - name: qid
        dtype: int32
      - name: qtext
        dtype: string
      - name: ra
        dtype: int32
      - name: image
        dtype: image
      - name: answers
        list:
          - name: aid
            dtype: int32
          - name: atext
            dtype: string
    splits:
      - name: train
        num_bytes: 1156808
        num_examples: 2657
      - name: test
        num_bytes: 1131536
        num_examples: 2742
      - name: validation
        num_bytes: 539892
        num_examples: 1366
    download_size: 79365502
    dataset_size: 2828236
config_names:
  - en
  - es

Dataset Card for HEAD-QA

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: HEAD-QA homepage
Repository: HEAD-QA repository
Paper: HEAD-QA: A Healthcare Dataset for Complex Reasoning
Leaderboard: HEAD-QA leaderboard
Point of Contact: María Grandury (Dataset Submitter)

Dataset Summary

HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio de Sanidad, Consumo y Bienestar Social, who also provides direct access to the exams of the last 5 years (in Spanish).

Date of the last update of the documents object of the reuse: January, 14th, 2019.

HEAD-QA tries to make these questions accesible for the Natural Language Processing community. We hope it is an useful resource towards achieving better QA systems. The dataset contains questions about the following topics:

Medicine
Nursing
Psychology
Chemistry
Pharmacology
Biology

Supported Tasks and Leaderboards

multiple-choice-qa: HEAD-QA is a multi-choice question answering testbed to encourage research on complex reasoning.

Languages

The questions and answers are available in both Spanish (BCP-47 code: 'es-ES') and English (BCP-47 code: 'en').

The language by default is Spanish:

from datasets import load_dataset

data_es = load_dataset('head_qa')

data_en = load_dataset('head_qa', 'en')

Dataset Structure

Data Instances

A typical data point comprises a question qtext, multiple possible answers atext and the right answer ra.

An example from the HEAD-QA dataset looks as follows:

{
'qid': '1', 
'category': 'biology', 
'qtext': 'Los potenciales postsinápticos excitadores:',
'answers': [
    {
        'aid': 1, 
        'atext': 'Son de tipo todo o nada.'
    }, 
    {
        'aid': 2, 
        'atext': 'Son hiperpolarizantes.'
    },
    {
        'aid': 3, 
        'atext': 'Se pueden sumar.'
    },
    {
        'aid': 4, 
        'atext': 'Se propagan a largas distancias.'
    },
    {
        'aid': 5, 
        'atext': 'Presentan un periodo refractario.'
    }],
'ra': '3',
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=675x538 at 0x1B42B6A1668>,
'name': 'Cuaderno_2013_1_B',
'year': '2013'
}

Data Fields

qid: question identifier (int)
category: category of the question: "medicine", "nursing", "psychology", "chemistry", "pharmacology", "biology"
qtext: question text
answers: list of possible answers. Each element of the list is a dictionary with 2 keys:
- aid: answer identifier (int)
- atext: answer text
ra: aid of the right answer (int)
image: (optional) a PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]
name: name of the exam from which the question was extracted
year: year in which the exam took place

Data Splits

The data is split into train, validation and test set for each of the two languages. The split sizes are as follow:

	Train	Val	Test
Spanish	2657	1366	2742
English	2657	1366	2742

Dataset Creation

Curation Rationale

As motivation for the creation of this dataset, here is the abstract of the paper:

"We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work."

Source Data

Initial Data Collection and Normalization

The questions come from exams to access a specialized position in the Spanish healthcare system, and are designed by the Ministerio de Sanidad, Consumo y Bienestar Social, who also provides direct access to the exams of the last 5 years (in Spanish).

Who are the source language producers?

The dataset was created by David Vilares and Carlos Gómez-Rodríguez.

Annotations

The dataset does not contain any additional annotations.

Annotation process

[N/A]

Who are the annotators?

[N/A]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

The dataset was created by David Vilares and Carlos Gómez-Rodríguez.

Licensing Information

According to the HEAD-QA homepage:

The Ministerio de Sanidad, Consumo y Biniestar Social allows the redistribution of the exams and their content under certain conditions:

The denaturalization of the content of the information is prohibited in any circumstance.
The user is obliged to cite the source of the documents subject to reuse.
The user is obliged to indicate the date of the last update of the documents object of the reuse.

According to the HEAD-QA repository:

The dataset is licensed under the MIT License.

Citation Information

@inproceedings{vilares-gomez-rodriguez-2019-head,
    title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
    author = "Vilares, David  and
      G{\'o}mez-Rodr{\'i}guez, Carlos",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1092",
    doi = "10.18653/v1/P19-1092",
    pages = "960--966",
    abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.",
}

Contributions

Thanks to @mariagrandury for adding this dataset.

Datasets:
head_qa

Dataset Card for HEAD-QA

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions