Datasets:
Tasks:
Multiple Choice
Modalities:
Text
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Size:
1K - 10K
License:
metadata
pretty_name: qa4mre
Dataset Card for "qa4mre"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: http://nlp.uned.es/clef-qa/repository/pastCampaigns.php
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 5.24 MB
- Size of the generated dataset: 46.11 MB
- Total amount of disk used: 51.35 MB
Dataset Summary
QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in question answering and reading comprehension. The dataset contains a supporting passage and a set of questions corresponding to the passage. Multiple options for answers are provided for each question, of which only one is correct. The training and test datasets are available for the main track. Additional gold standard documents are available for two pilot studies: one on alzheimers data, and the other on entrance exams data.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
2011.main.DE
- Size of downloaded dataset files: 0.21 MB
- Size of the generated dataset: 1.67 MB
- Total amount of disk used: 1.88 MB
An example of 'train' looks as follows.
2011.main.EN
- Size of downloaded dataset files: 0.19 MB
- Size of the generated dataset: 1.50 MB
- Total amount of disk used: 1.69 MB
An example of 'train' looks as follows.
2011.main.ES
- Size of downloaded dataset files: 0.21 MB
- Size of the generated dataset: 1.62 MB
- Total amount of disk used: 1.82 MB
An example of 'train' looks as follows.
2011.main.IT
- Size of downloaded dataset files: 0.20 MB
- Size of the generated dataset: 1.59 MB
- Total amount of disk used: 1.79 MB
An example of 'train' looks as follows.
2011.main.RO
- Size of downloaded dataset files: 0.21 MB
- Size of the generated dataset: 1.66 MB
- Total amount of disk used: 1.87 MB
An example of 'train' looks as follows.
Data Fields
The data fields are the same among all splits.
2011.main.DE
topic_id: astringfeature.topic_name: astringfeature.test_id: astringfeature.document_id: astringfeature.document_str: astringfeature.question_id: astringfeature.question_str: astringfeature.answer_options: a dictionary feature containing:answer_id: astringfeature.answer_str: astringfeature.
correct_answer_id: astringfeature.correct_answer_str: astringfeature.
2011.main.EN
topic_id: astringfeature.topic_name: astringfeature.test_id: astringfeature.document_id: astringfeature.document_str: astringfeature.question_id: astringfeature.question_str: astringfeature.answer_options: a dictionary feature containing:answer_id: astringfeature.answer_str: astringfeature.
correct_answer_id: astringfeature.correct_answer_str: astringfeature.
2011.main.ES
topic_id: astringfeature.topic_name: astringfeature.test_id: astringfeature.document_id: astringfeature.document_str: astringfeature.question_id: astringfeature.question_str: astringfeature.answer_options: a dictionary feature containing:answer_id: astringfeature.answer_str: astringfeature.
correct_answer_id: astringfeature.correct_answer_str: astringfeature.
2011.main.IT
topic_id: astringfeature.topic_name: astringfeature.test_id: astringfeature.document_id: astringfeature.document_str: astringfeature.question_id: astringfeature.question_str: astringfeature.answer_options: a dictionary feature containing:answer_id: astringfeature.answer_str: astringfeature.
correct_answer_id: astringfeature.correct_answer_str: astringfeature.
2011.main.RO
topic_id: astringfeature.topic_name: astringfeature.test_id: astringfeature.document_id: astringfeature.document_str: astringfeature.question_id: astringfeature.question_str: astringfeature.answer_options: a dictionary feature containing:answer_id: astringfeature.answer_str: astringfeature.
correct_answer_id: astringfeature.correct_answer_str: astringfeature.
Data Splits
| name | train |
|---|---|
| 2011.main.DE | 120 |
| 2011.main.EN | 120 |
| 2011.main.ES | 120 |
| 2011.main.IT | 120 |
| 2011.main.RO | 120 |
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
@InProceedings{10.1007/978-3-642-40802-1_29,
author="Pe{\~{n}}as, Anselmo
and Hovy, Eduard
and Forner, Pamela
and Rodrigo, {\'A}lvaro
and Sutcliffe, Richard
and Morante, Roser",
editor="Forner, Pamela
and M{\"u}ller, Henning
and Paredes, Roberto
and Rosso, Paolo
and Stein, Benno",
title="QA4MRE 2011-2013: Overview of Question Answering for Machine Reading Evaluation",
booktitle="Information Access Evaluation. Multilinguality, Multimodality, and Visualization",
year="2013",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="303--320",
abstract="This paper describes the methodology for testing the performance of Machine Reading systems through Question Answering and Reading Comprehension Tests. This was the attempt of the QA4MRE challenge which was run as a Lab at CLEF 2011--2013. The traditional QA task was replaced by a new Machine Reading task, whose intention was to ask questions that required a deep knowledge of individual short texts and in which systems were required to choose one answer, by analysing the corresponding test document in conjunction with background text collections provided by the organization. Four different tasks have been organized during these years: Main Task, Processing Modality and Negation for Machine Reading, Machine Reading of Biomedical Texts about Alzheimer's disease, and Entrance Exams. This paper describes their motivation, their goals, their methodology for preparing the data sets, their background collections, their metrics used for the evaluation, and the lessons learned along these three years.",
isbn="978-3-642-40802-1"
}
Contributions
Thanks to @patrickvonplaten, @albertvillanova, @mariamabarham, @thomwolf for adding this dataset.