metadata

annotations_creators:
  - no-annotation
language_creators:
  - other
languages:
  - ca
  - en
  - de
  - es
  - fr
  - ru
  - ja
  - it
  - zh
  - pt
  - nl
  - tr
  - pl
  - vi
  - ar
  - id
  - uk
  - ro
  - 'no'
  - th
  - sv
  - el
  - fi
  - he
  - da
  - cs
  - ko
  - fa
  - hi
  - hu
  - sk
  - lt
  - et
  - hr
  - is
  - lv
  - ms
  - bg
  - sr
  - ca
licenses:
  - cc0-1.0
multilinguality:
  - multilingual
pretty_name: MQA - a Multilingual FAQ and CQA Dataset
size_categories:
  - unknown
source_datasets:
  - original
task_categories:
  - question-answering
task_ids:
  - multiple-choice-qa

MQA

MQA is a multilingual corpus of questions and answers parsed from the Common Crawl. Questions are divided between Frequently Asked Questions (FAQ) pages and Community Question Answering (CQA) pages.

from datasets import load_dataset
all = load_dataset("clips/mqa", language="en")
{
  "name": "the title of the question (if any)",
  "text": "the body of the question (if any)",
  "answers": [{
    "text": "the text of the answer",
    "is_accepted": "true|false"
  }]
}
faq = load_dataset("clips/mqa", scope="faq", language="en")
cqa = load_dataset("clips/mqa", scope="cqa", language="en")

Languages

We collected around 234M pairs of questions and answers in 39 languages. To download a language specific subset you need to specify the language key as configuration. See below for an example.

load_dataset("clips/mqa", language="en") # replace "en" by any language listed below

Language	Questions	Answers	Pages	Domains
en	137,822,939	151,236,638	28,384,357	175,026
de	12,991,469	14,677,399	3,440,491	29,784
es	11,074,459	12,100,541	2,843,291	18,743
fr	10,317,588	14,952,761	3,060,417	19,138
ru	10,066,536	12,672,886	4,031,474	18,880
ja	5,937,927	8,854,124	2,387,580	11,398
it	5,054,493	5,812,885	1,338,489	9,295
zh	5,005,599	5,741,654	1,358,906	5,283
pt	4,213,838	4,525,732	1,224,703	6,908
nl	3,724,794	4,633,492	1,195,595	13,736
tr	3,044,839	4,378,115	894,573	6,178
pl	2,924,085	3,038,402	744,734	6,018
vi	2,103,839	2,190,328	525,418	6,634
ar	2,044,049	2,485,039	707,190	2,876
id	1,701,544	1,852,378	553,555	5,191
uk	1,432,987	1,448,816	389,539	3,382
ro	1,421,447	1,708,864	438,946	2,358
no	1,279,763	1,288,813	279,882	3,511
th	1,208,446	1,248,881	267,713	1,898
sv	1,138,954	1,146,543	240,501	4,405
el	1,125,998	1,134,091	209,976	1,849
fi	1,013,833	1,025,872	213,433	2,266
he	988,660	1,009,356	217,704	2,714
da	965,548	976,758	216,114	3,942
cs	859,778	992,455	260,186	2,440
ko	824,789	867,748	201,321	2,409
fa	716,294	835,540	319,874	7,679
hi	607,882	1,034,200	236,490	1,799
hu	508,450	515,383	137,361	2,094
sk	474,853	484,772	98,872	1,052
lt	397,850	397,914	80,184	596
et	286,598	287,578	64,601	668
hr	234,166	239,068	56,883	1,559
is	219,783	219,787	37,111	117
lv	213,581	213,661	39,320	373
ms	172,940	174,498	41,280	551
bg	118,083	122,536	28,423	1,757
sr	95,945	98,307	31,143	1,219
ca	67,061	68,216	13,409	550

FAQ vs. CQA

You can download the Frequently Asked Questions (FAQ) or the Community Question Answering (CQA) part of the dataset.

faq = load_dataset("clips/mqa", scope="faq")
cqa = load_dataset("clips/mqa", scope="cqa")
all = load_dataset("clips/mqa", scope="all")

Although FAQ and CQA questions share the same structure, CQA questions can have multiple answers for a given questions, while FAQ questions have a single answer. FAQ questions typically only have a title (name key), while CQA have a title and a body (name and text).

Nesting and Data Fields

You can specify three different nesting level: question, page and domain.

Question

load_dataset("clips/mqa", level="question") # default

The default level is the question object:

name: the title of the question (if any)
text: the body of the question (if any)
answers: a list of answers
- text: the title of the answer (if any)
- name: the body of the answer
- is_accepted: true if the answer is selected.

Page

This level returns a list of questions present on the same page. This is mostly useful for FAQs since CQAs already have one question per page.

load_dataset("clips/mqa", level="page")

Domain

This level returns a list of pages present on the web domain. This is a good way to cope with FAQs duplication by sampling one page per domain at each epoch.

load_dataset("clips/mqa", level="domain")

Source Data

This section was adapted from the source data description of OSCAR

Common Crawl is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected nofollow and robots.txt policies.

To construct MQA, we used the WARC files of Common Crawl.

People

This model was developed by Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann and Walter Daelemans.

Licensing Information

These data are released under this licensing scheme.
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/

Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.

We will comply to legitimate requests by removing the affected sources from the next release of the corpus.

Citation information

@misc{debruyn2021mfaq,
      title={MFAQ: a Multilingual FAQ Dataset}, 
      author={Maxime {De Bruyn} and Ehsan Lotfi and Jeska Buhmann and Walter Daelemans},
      year={2021},
      eprint={2109.12870},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}