Datasets:
The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets library). If this is not possible, please
open a discussion
for direct help.
Dataset Card for "mlqa"
Dataset Summary
MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance.
MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic,
German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between
4 different languages on average.
Supported Tasks and Leaderboards
Languages
MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese.
Dataset Structure
Data Instances
mlqa-translate-test.ar
- Size of downloaded dataset files: 10.08 MB
- Size of the generated dataset: 5.48 MB
- Total amount of disk used: 15.56 MB
An example of 'test' looks as follows.
mlqa-translate-test.de
- Size of downloaded dataset files: 10.08 MB
- Size of the generated dataset: 3.88 MB
- Total amount of disk used: 13.96 MB
An example of 'test' looks as follows.
mlqa-translate-test.es
- Size of downloaded dataset files: 10.08 MB
- Size of the generated dataset: 3.92 MB
- Total amount of disk used: 13.99 MB
An example of 'test' looks as follows.
mlqa-translate-test.hi
- Size of downloaded dataset files: 10.08 MB
- Size of the generated dataset: 4.61 MB
- Total amount of disk used: 14.68 MB
An example of 'test' looks as follows.
mlqa-translate-test.vi
- Size of downloaded dataset files: 10.08 MB
- Size of the generated dataset: 6.00 MB
- Total amount of disk used: 16.07 MB
An example of 'test' looks as follows.
Data Fields
The data fields are the same among all splits.
mlqa-translate-test.ar
context: astringfeature.question: astringfeature.answers: a dictionary feature containing:answer_start: aint32feature.text: astringfeature.
id: astringfeature.
mlqa-translate-test.de
context: astringfeature.question: astringfeature.answers: a dictionary feature containing:answer_start: aint32feature.text: astringfeature.
id: astringfeature.
mlqa-translate-test.es
context: astringfeature.question: astringfeature.answers: a dictionary feature containing:answer_start: aint32feature.text: astringfeature.
id: astringfeature.
mlqa-translate-test.hi
context: astringfeature.question: astringfeature.answers: a dictionary feature containing:answer_start: aint32feature.text: astringfeature.
id: astringfeature.
mlqa-translate-test.vi
context: astringfeature.question: astringfeature.answers: a dictionary feature containing:answer_start: aint32feature.text: astringfeature.
id: astringfeature.
Data Splits
| name | test |
|---|---|
| mlqa-translate-test.ar | 5335 |
| mlqa-translate-test.de | 4517 |
| mlqa-translate-test.es | 5253 |
| mlqa-translate-test.hi | 4918 |
| mlqa-translate-test.vi | 5495 |
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
@article{lewis2019mlqa,
title = {MLQA: Evaluating Cross-lingual Extractive Question Answering},
author = {Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
journal = {arXiv preprint arXiv:1910.07475},
year = 2019,
eid = {arXiv: 1910.07475}
}
Contributions
Thanks to @patrickvonplaten, @M-Salti, @lewtun, @thomwolf, @mariamabarham, @lhoestq for adding this dataset.
license: apache-2.0
- Downloads last month
- 34