Datasets:

microsoft
/

xglue

	---
	annotations_creators:
	mlqa:
	- crowdsourced
	nc:
	- machine-generated
	ner:
	- expert-generated
	- found
	ntg:
	- machine-generated
	paws-x:
	- expert-generated
	pos:
	- expert-generated
	- found
	qadsm:
	- machine-generated
	qam:
	- machine-generated
	qg:
	- machine-generated
	wpr:
	- machine-generated
	xnli:
	- machine-generated
	language_creators:
	mlqa:
	- found
	nc:
	- found
	ner:
	- crowdsourced
	- expert-generated
	ntg:
	- machine-generated
	paws-x:
	- expert-generated
	pos:
	- crowdsourced
	- expert-generated
	qadsm:
	- found
	qam:
	- found
	qg:
	- machine-generated
	wpr:
	- found
	xnli:
	- crowdsourced
	- expert-generated
	languages:
	mlqa:
	- ar
	- de
	- en
	- es
	- hi
	- vi
	- zh
	nc:
	- en
	- de
	- es
	- fr
	- ru
	ner:
	- de
	- en
	- es
	- nl
	ntg:
	- en
	- de
	- es
	- fr
	- ru
	paws-x:
	- en
	- de
	- es
	- fr
	pos:
	- ar
	- bg
	- de
	- el
	- en
	- es
	- fr
	- hi
	- it
	- nl
	- pl
	- ru
	- th
	- tr
	- ur
	- vi
	- zh
	qadsm:
	- en
	- de
	- fr
	qam:
	- en
	- de
	- fr
	qg:
	- en
	- de
	- fr
	- pt
	- it
	- zh
	wpr:
	- en
	- de
	- fr
	- es
	- it
	- pt
	- zh
	xnli:
	- ar
	- bg
	- de
	- el
	- en
	- es
	- fr
	- hi
	- ru
	- sw
	- th
	- tr
	- ur
	- vi
	- zh
	licenses:
	mlqa:
	- cc-by-sa-4-0
	nc:
	- unknown
	ner:
	- unknown
	ntg:
	- unknown
	paws-x:
	- unknown
	pos:
	- other-Licence Universal Dependencies v2-5
	qadsm:
	- unknown
	qam:
	- unknown
	qg:
	- unknown
	wpr:
	- unknown
	xnli:
	- cc-by-nc-4-0
	multilinguality:
	mlqa:
	- multilingual
	nc:
	- multilingual
	ner:
	- multilingual
	ntg:
	- multilingual
	paws-x:
	- multilingual
	pos:
	- multilingual
	qadsm:
	- multilingual
	qam:
	- multilingual
	qg:
	- multilingual
	wpr:
	- multilingual
	xnli:
	- multilingual
	- translation
	size_categories:
	mlqa:
	- 100K<n<1M
	nc:
	- 100K<n<1M
	ner:
	- 10K<n<100K
	ntg:
	- 100K<n<1M
	paws-x:
	- 10K<n<100K
	pos:
	- 10K<n<100K
	qadsm:
	- 100K<n<1M
	qam:
	- 100K<n<1M
	qg:
	- 100K<n<1M
	wpr:
	- 100K<n<1M
	xnli:
	- 100K<n<1M
	source_datasets:
	mlqa:
	- extended\|squad
	nc:
	- original
	ner:
	- extended\|conll2003
	ntg:
	- original
	paws-x:
	- original
	pos:
	- original
	qadsm:
	- original
	qam:
	- original
	qg:
	- original
	wpr:
	- original
	xnli:
	- extended\|xnli
	task_categories:
	mlqa:
	- question-answering
	nc:
	- text-classification
	ner:
	- token-classification
	ntg:
	- summarization
	paws-x:
	- text-classification
	pos:
	- token-classification
	qadsm:
	- text-classification
	qam:
	- text-classification
	qg:
	- text2text-generation
	wpr:
	- text-classification
	xnli:
	- text-classification
	task_ids:
	mlqa:
	- extractive-qa
	- open-domain-qa
	nc:
	- topic-classification
	ner:
	- named-entity-recognition
	ntg:
	- news-articles-headline-generation
	paws-x:
	- text-classification-other-paraphrase-identification
	pos:
	- parsing
	qadsm:
	- acceptability-classification
	qam:
	- acceptability-classification
	qg:
	- text2text-generation-other-question-answering
	wpr:
	- acceptability-classification
	xnli:
	- natural-language-inference
	paperswithcode_id: null
	pretty_name: XGLUE
	---

	# Dataset Card for XGLUE

	## Table of Contents
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [XGLUE homepage](https://microsoft.github.io/XGLUE/)
	- Paper: [XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation](https://arxiv.org/abs/1907.09190)

	### Dataset Summary

	XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained models with respect to cross-lingual natural language understanding and generation.

	The training data of each task is in English while the validation and test data is present in multiple different languages.
	The following table shows which languages are present as validation and test data for each config.

	![Available Languages for Test and Validation Data](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xglue_langs.png)

	Therefore, for each config, a cross-lingual pre-trained model should be fine-tuned on the English training data, and evaluated on for all languages.

	### Supported Tasks and Leaderboards

	The XGLUE leaderboard can be found on the [homepage](https://microsoft.github.io/XGLUE/) and
	consits of a XGLUE-Understanding Score (the average of the tasks `ner`, `pos`, `mlqa`, `nc`, `xnli`, `paws-x`, `qadsm`, `wpr`, `qam`) and a XGLUE-Generation Score (the average of the tasks `qg`, `ntg`).

	### Languages

	For all tasks (configurations), the "train" split is in English (`en`).

	For each task, the "validation" and "test" splits are present in these languages:
	- ner: `en`, `de`, `es`, `nl`
	- pos: `en`, `de`, `es`, `nl`, `bg`, `el`, `fr`, `pl`, `tr`, `vi`, `zh`, `ur`, `hi`, `it`, `ar`, `ru`, `th`
	- mlqa: `en`, `de`, `ar`, `es`, `hi`, `vi`, `zh`
	- nc: `en`, `de`, `es`, `fr`, `ru`
	- xnli: `en`, `ar`, `bg`, `de`, `el`, `es`, `fr`, `hi`, `ru`, `sw`, `th`, `tr`, `ur`, `vi`, `zh`
	- paws-x: `en`, `de`, `es`, `fr`
	- qadsm: `en`, `de`, `fr`
	- wpr: `en`, `de`, `es`, `fr`, `it`, `pt`, `zh`
	- qam: `en`, `de`, `fr`
	- qg: `en`, `de`, `es`, `fr`, `it`, `pt`
	- ntg: `en`, `de`, `es`, `fr`, `ru`

	## Dataset Structure

	### Data Instances

	#### ner

	An example of 'test.nl' looks as follows.

	```
	{
	"ner": [
	"O",
	"O",
	"O",
	"B-LOC",
	"O",
	"B-LOC",
	"O",
	"B-LOC",
	"O",
	"O",
	"O",
	"O",
	"O",
	"O",
	"O",
	"B-PER",
	"I-PER",
	"O",
	"O",
	"B-LOC",
	"O",
	"O"
	],
	"words": [
	"Dat",
	"is",
	"in",
	"Itali\u00eb",
	",",
	"Spanje",
	"of",
	"Engeland",
	"misschien",
	"geen",
	"probleem",
	",",
	"maar",
	"volgens",
	"'",
	"Der",
	"Kaiser",
	"'",
	"in",
	"Duitsland",
	"wel",
	"."
	]
	}
	```

	#### pos

	An example of 'test.fr' looks as follows.

	```
	{
	"pos": [
	"PRON",
	"VERB",
	"SCONJ",
	"ADP",
	"PRON",
	"CCONJ",
	"DET",
	"NOUN",
	"ADP",
	"NOUN",
	"CCONJ",
	"NOUN",
	"ADJ",
	"PRON",
	"PRON",
	"AUX",
	"ADV",
	"VERB",
	"PUNCT",
	"PRON",
	"VERB",
	"VERB",
	"DET",
	"ADJ",
	"NOUN",
	"ADP",
	"DET",
	"NOUN",
	"PUNCT"
	],
	"words": [
	"Je",
	"sens",
	"qu'",
	"entre",
	"\u00e7a",
	"et",
	"les",
	"films",
	"de",
	"m\u00e9decins",
	"et",
	"scientifiques",
	"fous",
	"que",
	"nous",
	"avons",
	"d\u00e9j\u00e0",
	"vus",
	",",
	"nous",
	"pourrions",
	"emprunter",
	"un",
	"autre",
	"chemin",
	"pour",
	"l'",
	"origine",
	"."
	]
	}
	```

	#### mlqa

	An example of 'test.hi' looks as follows.

	```
	{
	"answers": {
	"answer_start": [
	378
	],
	"text": [
	"\u0909\u0924\u094d\u0924\u0930 \u092a\u0942\u0930\u094d\u0935"
	]
	},
	"context": "\u0909\u0938\u0940 \"\u090f\u0930\u093f\u092f\u093e XX \" \u0928\u093e\u092e\u0915\u0930\u0923 \u092a\u094d\u0930\u0923\u093e\u0932\u0940 \u0915\u093e \u092a\u094d\u0930\u092f\u094b\u0917 \u0928\u0947\u0935\u093e\u0926\u093e \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u094d\u0925\u0932 \u0915\u0947 \u0905\u0928\u094d\u092f \u092d\u093e\u0917\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u0915\u093f\u092f\u093e \u0917\u092f\u093e \u0939\u0948\u0964\u092e\u0942\u0932 \u0930\u0942\u092a \u092e\u0947\u0902 6 \u092c\u091f\u0947 10 \u092e\u0940\u0932 \u0915\u093e \u092f\u0939 \u0906\u092f\u0924\u093e\u0915\u093e\u0930 \u0905\u0921\u094d\u0921\u093e \u0905\u092c \u0924\u0925\u093e\u0915\u0925\u093f\u0924 '\u0917\u094d\u0930\u0942\u092e \u092c\u0949\u0915\u094d\u0938 \" \u0915\u093e \u090f\u0915 \u092d\u093e\u0917 \u0939\u0948, \u091c\u094b \u0915\u093f 23 \u092c\u091f\u0947 25.3 \u092e\u0940\u0932 \u0915\u093e \u090f\u0915 \u092a\u094d\u0930\u0924\u093f\u092c\u0902\u0927\u093f\u0924 \u0939\u0935\u093e\u0908 \u0915\u094d\u0937\u0947\u0924\u094d\u0930 \u0939\u0948\u0964 \u092f\u0939 \u0915\u094d\u0937\u0947\u0924\u094d\u0930 NTS \u0915\u0947 \u0906\u0902\u0924\u0930\u093f\u0915 \u0938\u0921\u093c\u0915 \u092a\u094d\u0930\u092c\u0902\u0927\u0928 \u0938\u0947 \u091c\u0941\u0921\u093c\u093e \u0939\u0948, \u091c\u093f\u0938\u0915\u0940 \u092a\u0915\u094d\u0915\u0940 \u0938\u0921\u093c\u0915\u0947\u0902 \u0926\u0915\u094d\u0937\u093f\u0923 \u092e\u0947\u0902 \u092e\u0930\u0915\u0930\u0940 \u0915\u0940 \u0913\u0930 \u0914\u0930 \u092a\u0936\u094d\u091a\u093f\u092e \u092e\u0947\u0902 \u092f\u0941\u0915\u094d\u0915\u093e \u092b\u094d\u0932\u0948\u091f \u0915\u0940 \u0913\u0930 \u091c\u093e\u0924\u0940 \u0939\u0948\u0902\u0964 \u091d\u0940\u0932 \u0938\u0947 \u0909\u0924\u094d\u0924\u0930 \u092a\u0942\u0930\u094d\u0935 \u0915\u0940 \u0913\u0930 \u092c\u0922\u093c\u0924\u0947 \u0939\u0941\u090f \u0935\u094d\u092f\u093e\u092a\u0915 \u0914\u0930 \u0914\u0930 \u0938\u0941\u0935\u094d\u092f\u0935\u0938\u094d\u0925\u093f\u0924 \u0917\u094d\u0930\u0942\u092e \u091d\u0940\u0932 \u0915\u0940 \u0938\u0921\u093c\u0915\u0947\u0902 \u090f\u0915 \u0926\u0930\u094d\u0930\u0947 \u0915\u0947 \u091c\u0930\u093f\u092f\u0947 \u092a\u0947\u091a\u0940\u0926\u093e \u092a\u0939\u093e\u0921\u093c\u093f\u092f\u094b\u0902 \u0938\u0947 \u0939\u094b\u0915\u0930 \u0917\u0941\u091c\u0930\u0924\u0940 \u0939\u0948\u0902\u0964 \u092a\u0939\u0932\u0947 \u0938\u0921\u093c\u0915\u0947\u0902 \u0917\u094d\u0930\u0942\u092e \u0918\u093e\u091f\u0940",
	"question": "\u091d\u0940\u0932 \u0915\u0947 \u0938\u093e\u092a\u0947\u0915\u094d\u0937 \u0917\u094d\u0930\u0942\u092e \u0932\u0947\u0915 \u0930\u094b\u0921 \u0915\u0939\u093e\u0901 \u091c\u093e\u0924\u0940 \u0925\u0940?"
	}
	```

	#### nc

	An example of 'test.es' looks as follows.

	```
	{
	"news_body": "El bizcocho es seguramente el producto m\u00e1s b\u00e1sico y sencillo de toda la reposter\u00eda : consiste en poco m\u00e1s que mezclar unos cuantos ingredientes, meterlos al horno y esperar a que se hagan. Por obra y gracia del impulsor qu\u00edmico, tambi\u00e9n conocido como \"levadura de tipo Royal\", despu\u00e9s de un rato de calorcito esta combinaci\u00f3n de harina, az\u00facar, huevo, grasa -aceite o mantequilla- y l\u00e1cteo se transforma en uno de los productos m\u00e1s deliciosos que existen para desayunar o merendar . Por muy manazas que seas, es m\u00e1s que probable que tu bizcocho casero supere en calidad a cualquier infamia industrial envasada. Para lograr un bizcocho digno de admiraci\u00f3n s\u00f3lo tienes que respetar unas pocas normas que afectan a los ingredientes, proporciones, mezclado, horneado y desmoldado. Todas las tienes resumidas en unos dos minutos el v\u00eddeo de arriba, en el que adem \u00e1s aprender\u00e1s alg\u00fan truquillo para que tu bizcochaco quede m\u00e1s fino, jugoso, esponjoso y amoroso. M\u00e1s en MSN:",
	"news_category": "foodanddrink",
	"news_title": "Cocina para lerdos: las leyes del bizcocho"
	}
	```

	#### xnli

	An example of 'validation.th' looks as follows.

	```
	{
	"hypothesis": "\u0e40\u0e02\u0e32\u0e42\u0e17\u0e23\u0e2b\u0e32\u0e40\u0e40\u0e21\u0e48\u0e02\u0e2d\u0e07\u0e40\u0e02\u0e32\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e23\u0e27\u0e14\u0e40\u0e23\u0e47\u0e27\u0e2b\u0e25\u0e31\u0e07\u0e08\u0e32\u0e01\u0e17\u0e35\u0e48\u0e23\u0e16\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e48\u0e07\u0e40\u0e02\u0e32\u0e40\u0e40\u0e25\u0e49\u0e27",
	"label": 1,
	"premise": "\u0e41\u0e25\u0e30\u0e40\u0e02\u0e32\u0e1e\u0e39\u0e14\u0e27\u0e48\u0e32, \u0e21\u0e48\u0e32\u0e21\u0e4a\u0e32 \u0e1c\u0e21\u0e2d\u0e22\u0e39\u0e48\u0e1a\u0e49\u0e32\u0e19"
	}
	```

	#### paws-x

	An example of 'test.es' looks as follows.

	```
	{
	"label": 1,
	"sentence1": "La excepci\u00f3n fue entre fines de 2005 y 2009 cuando jug\u00f3 en Suecia con Carlstad United BK, Serbia con FK Borac \u010ca\u010dak y el FC Terek Grozny de Rusia.",
	"sentence2": "La excepci\u00f3n se dio entre fines del 2005 y 2009, cuando jug\u00f3 con Suecia en el Carlstad United BK, Serbia con el FK Borac \u010ca\u010dak y el FC Terek Grozny de Rusia."
	}
	```

	#### qadsm

	An example of 'train' looks as follows.

	```
	{
	"ad_description": "Your New England Cruise Awaits! Holland America Line Official Site.",
	"ad_title": "New England Cruises",
	"query": "cruise portland maine",
	"relevance_label": 1
	}
	```

	#### wpr

	An example of 'test.zh' looks as follows.

	```
	{
	"query": "maxpro\u5b98\u7f51",
	"relavance_label": 0,
	"web_page_snippet": "\u5728\u7ebf\u8d2d\u4e70\uff0c\u552e\u540e\u670d\u52a1\u3002vivo\u667a\u80fd\u624b\u673a\u5f53\u5b63\u660e\u661f\u673a\u578b\u6709NEX\uff0cvivo X21\uff0cvivo X20\uff0c\uff0cvivo X23\u7b49\uff0c\u5728vivo\u5b98\u7f51\u8d2d\u4e70\u624b\u673a\u53ef\u4ee5\u4eab\u53d712 \u671f\u514d\u606f\u4ed8\u6b3e\u3002 \u54c1\u724c Funtouch OS \u4f53\u9a8c\u5e97 \| ...",
	"wed_page_title": "vivo\u667a\u80fd\u624b\u673a\u5b98\u65b9\u7f51\u7ad9-AI\u975e\u51e1\u6444\u5f71X23"
	}
	```

	#### qam

	An example of 'validation.en' looks as follows.

	```
	{
	"annswer": "Erikson has stated that after the last novel of the Malazan Book of the Fallen was finished, he and Esslemont would write a comprehensive guide tentatively named The Encyclopaedia Malazica.",
	"label": 0,
	"question": "main character of malazan book of the fallen"
	}
	```

	#### qg

	An example of 'test.de' looks as follows.

	```
	{
	"answer_passage": "Medien bei WhatsApp automatisch speichern. Tippen Sie oben rechts unter WhatsApp auf die drei Punkte oder auf die Men\u00fc-Taste Ihres Smartphones. Dort wechseln Sie in die \"Einstellungen\" und von hier aus weiter zu den \"Chat-Einstellungen\". Unter dem Punkt \"Medien Auto-Download\" k\u00f6nnen Sie festlegen, wann die WhatsApp-Bilder heruntergeladen werden sollen.",
	"question": "speichenn von whats app bilder unterbinden"
	}
	```

	#### ntg

	An example of 'test.en' looks as follows.

	```
	{
	"news_body": "Check out this vintage Willys Pickup! As they say, the devil is in the details, and it's not every day you see such attention paid to every last area of a restoration like with this 1961 Willys Pickup . Already the Pickup has a unique look that shares some styling with the Jeep, plus some original touches you don't get anywhere else. It's a classy way to show up to any event, all thanks to Hollywood Motors . A burgundy paint job contrasts with white lower panels and the roof. Plenty of tasteful chrome details grace the exterior, including the bumpers, headlight bezels, crossmembers on the grille, hood latches, taillight bezels, exhaust finisher, tailgate hinges, etc. Steel wheels painted white and chrome hubs are a tasteful addition. Beautiful oak side steps and bed strips add a touch of craftsmanship to this ride. This truck is of real showroom quality, thanks to the astoundingly detailed restoration work performed on it, making this Willys Pickup a fierce contender for best of show. Under that beautiful hood is a 225 Buick V6 engine mated to a three-speed manual transmission, so you enjoy an ideal level of control. Four wheel drive is functional, making it that much more utilitarian and downright cool. The tires are new, so you can enjoy a lot of life out of them, while the wheels and hubs are in great condition. Just in case, a fifth wheel with a tire and a side mount are included. Just as important, this Pickup runs smoothly, so you can go cruising or even hit the open road if you're interested in participating in some classic rallies. You might associate Willys with the famous Jeep CJ, but the automaker did produce a fair amount of trucks. The Pickup is quite the unique example, thanks to distinct styling that really turns heads, making it a favorite at quite a few shows. Source: Hollywood Motors Check These Rides Out Too: Fear No Trails With These Off-Roaders 1965 Pontiac GTO: American Icon For Sale In Canada Low-Mileage 1955 Chevy 3100 Represents Turn In Pickup Market",
	"news_title": "This 1961 Willys Pickup Will Let You Cruise In Style"
	}
	```

	### Data Fields

	#### ner

	In the following each data field in ner is explained. The data fields are the same among all splits.

	- `words`: a list of words composing the sentence.
	- `ner`: a list of entitity classes corresponding to each word respectively.


	#### pos

	In the following each data field in pos is explained. The data fields are the same among all splits.

	- `words`: a list of words composing the sentence.
	- `pos`: a list of "part-of-speech" classes corresponding to each word respectively.


	#### mlqa

	In the following each data field in mlqa is explained. The data fields are the same among all splits.

	- `context`: a string, the context containing the answer.
	- `question`: a string, the question to be answered.
	- `answers`: a string, the answer to `question`.


	#### nc

	In the following each data field in nc is explained. The data fields are the same among all splits.

	- `news_title`: a string, to the title of the news report.
	- `news_body`: a string, to the actual news report.
	- `news_category`: a string, the category of the news report, e.g. `foodanddrink`


	#### xnli

	In the following each data field in xnli is explained. The data fields are the same among all splits.

	- `premise`: a string, the context/premise, i.e. the first sentence for natural language inference.
	- `hypothesis`: a string, a sentence whereas its relation to `premise` is to be classified, i.e. the second sentence for natural language inference.
	- `label`: a class catory (int), natural language inference relation class between `hypothesis` and `premise`. One of 0: entailment, 1: contradiction, 2: neutral.


	#### paws-x

	In the following each data field in paws-x is explained. The data fields are the same among all splits.

	- `sentence1`: a string, a sentence.
	- `sentence2`: a string, a sentence whereas the sentence is either a paraphrase of `sentence1` or not.
	- `label`: a class label (int), whether `sentence2` is a paraphrase of `sentence1` One of 0: different, 1: same.


	#### qadsm

	In the following each data field in qadsm is explained. The data fields are the same among all splits.

	- `query`: a string, the search query one would insert into a search engine.
	- `ad_title`: a string, the title of the advertisement.
	- `ad_description`: a string, the content of the advertisement, i.e. the main body.
	- `relevance_label`: a class label (int), how relevant the advertisement `ad_title` + `ad_description` is to the search query `query`. One of 0: Bad, 1: Good.


	#### wpr

	In the following each data field in wpr is explained. The data fields are the same among all splits.

	- `query`: a string, the search query one would insert into a search engine.
	- `web_page_title`: a string, the title of a web page.
	- `web_page_snippet`: a string, the content of a web page, i.e. the main body.
	- `relavance_label`: a class label (int), how relevant the web page `web_page_snippet` + `web_page_snippet` is to the search query `query`. One of 0: Bad, 1: Fair, 2: Good, 3: Excellent, 4: Perfect.


	#### qam

	In the following each data field in qam is explained. The data fields are the same among all splits.

	- `question`: a string, a question.
	- `answer`: a string, a possible answer to `question`.
	- `label`: a class label (int), whether the `answer` is relevant to the `question`. One of 0: False, 1: True.


	#### qg

	In the following each data field in qg is explained. The data fields are the same among all splits.

	- `answer_passage`: a string, a detailed answer to the `question`.
	- `question`: a string, a question.


	#### ntg

	In the following each data field in ntg is explained. The data fields are the same among all splits.

	- `news_body`: a string, the content of a news article.
	- `news_title`: a string, the title corresponding to the news article `news_body`.


	### Data Splits

	#### ner

	The following table shows the number of data samples/number of rows for each split in ner.

	\| \|train\|validation.en\|validation.de\|validation.es\|validation.nl\|test.en\|test.de\|test.es\|test.nl\|
	\|---\|----:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|
	\|ner\|14042\| 3252\| 2874\| 1923\| 2895\| 3454\| 3007\| 1523\| 5202\|


	#### pos

	The following table shows the number of data samples/number of rows for each split in pos.

	\| \|train\|validation.en\|validation.de\|validation.es\|validation.nl\|validation.bg\|validation.el\|validation.fr\|validation.pl\|validation.tr\|validation.vi\|validation.zh\|validation.ur\|validation.hi\|validation.it\|validation.ar\|validation.ru\|validation.th\|test.en\|test.de\|test.es\|test.nl\|test.bg\|test.el\|test.fr\|test.pl\|test.tr\|test.vi\|test.zh\|test.ur\|test.hi\|test.it\|test.ar\|test.ru\|test.th\|
	\|---\|----:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|
	\|pos\|25376\| 2001\| 798\| 1399\| 717\| 1114\| 402\| 1475\| 2214\| 987\| 799\| 499\| 551\| 1658\| 563\| 908\| 578\| 497\| 2076\| 976\| 425\| 595\| 1115\| 455\| 415\| 2214\| 982\| 799\| 499\| 534\| 1683\| 481\| 679\| 600\| 497\|


	#### mlqa

	The following table shows the number of data samples/number of rows for each split in mlqa.

	\| \|train\|validation.en\|validation.de\|validation.ar\|validation.es\|validation.hi\|validation.vi\|validation.zh\|test.en\|test.de\|test.ar\|test.es\|test.hi\|test.vi\|test.zh\|
	\|----\|----:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|
	\|mlqa\|87599\| 1148\| 512\| 517\| 500\| 507\| 511\| 504\| 11590\| 4517\| 5335\| 5253\| 4918\| 5495\| 5137\|


	#### nc

	The following table shows the number of data samples/number of rows for each split in nc.

	\| \|train \|validation.en\|validation.de\|validation.es\|validation.fr\|validation.ru\|test.en\|test.de\|test.es\|test.fr\|test.ru\|
	\|---\|-----:\|------------:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|------:\|
	\|nc \|100000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\|


	#### xnli

	The following table shows the number of data samples/number of rows for each split in xnli.

	\| \|train \|validation.en\|validation.ar\|validation.bg\|validation.de\|validation.el\|validation.es\|validation.fr\|validation.hi\|validation.ru\|validation.sw\|validation.th\|validation.tr\|validation.ur\|validation.vi\|validation.zh\|test.en\|test.ar\|test.bg\|test.de\|test.el\|test.es\|test.fr\|test.hi\|test.ru\|test.sw\|test.th\|test.tr\|test.ur\|test.vi\|test.zh\|
	\|----\|-----:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|
	\|xnli\|392702\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\|


	#### nc

	The following table shows the number of data samples/number of rows for each split in nc.

	\| \|train \|validation.en\|validation.de\|validation.es\|validation.fr\|validation.ru\|test.en\|test.de\|test.es\|test.fr\|test.ru\|
	\|---\|-----:\|------------:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|------:\|
	\|nc \|100000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\|


	#### xnli

	The following table shows the number of data samples/number of rows for each split in xnli.

	\| \|train \|validation.en\|validation.ar\|validation.bg\|validation.de\|validation.el\|validation.es\|validation.fr\|validation.hi\|validation.ru\|validation.sw\|validation.th\|validation.tr\|validation.ur\|validation.vi\|validation.zh\|test.en\|test.ar\|test.bg\|test.de\|test.el\|test.es\|test.fr\|test.hi\|test.ru\|test.sw\|test.th\|test.tr\|test.ur\|test.vi\|test.zh\|
	\|----\|-----:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|
	\|xnli\|392702\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 2490\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\| 5010\|


	#### paws-x

	The following table shows the number of data samples/number of rows for each split in paws-x.

	\| \|train\|validation.en\|validation.de\|validation.es\|validation.fr\|test.en\|test.de\|test.es\|test.fr\|
	\|------\|----:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|
	\|paws-x\|49401\| 2000\| 2000\| 2000\| 2000\| 2000\| 2000\| 2000\| 2000\|


	#### qadsm

	The following table shows the number of data samples/number of rows for each split in qadsm.

	\| \|train \|validation.en\|validation.de\|validation.fr\|test.en\|test.de\|test.fr\|
	\|-----\|-----:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|
	\|qadsm\|100000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\|


	#### wpr

	The following table shows the number of data samples/number of rows for each split in wpr.

	\| \|train\|validation.en\|validation.de\|validation.es\|validation.fr\|validation.it\|validation.pt\|validation.zh\|test.en\|test.de\|test.es\|test.fr\|test.it\|test.pt\|test.zh\|
	\|---\|----:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|------:\|------:\|------:\|
	\|wpr\|99997\| 10008\| 10004\| 10004\| 10005\| 10003\| 10001\| 10002\| 10004\| 9997\| 10006\| 10020\| 10001\| 10015\| 9999\|


	#### qam

	The following table shows the number of data samples/number of rows for each split in qam.

	\| \|train \|validation.en\|validation.de\|validation.fr\|test.en\|test.de\|test.fr\|
	\|---\|-----:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|
	\|qam\|100000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\|


	#### qg

	The following table shows the number of data samples/number of rows for each split in qg.

	\| \|train \|validation.en\|validation.de\|validation.es\|validation.fr\|validation.it\|validation.pt\|test.en\|test.de\|test.es\|test.fr\|test.it\|test.pt\|
	\|---\|-----:\|------------:\|------------:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|------:\|------:\|
	\|qg \|100000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\|


	#### ntg

	The following table shows the number of data samples/number of rows for each split in ntg.

	\| \|train \|validation.en\|validation.de\|validation.es\|validation.fr\|validation.ru\|test.en\|test.de\|test.es\|test.fr\|test.ru\|
	\|---\|-----:\|------------:\|------------:\|------------:\|------------:\|------------:\|------:\|------:\|------:\|------:\|------:\|
	\|ntg\|300000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\| 10000\|

	## Dataset Creation

	### Curation Rationale

	[More Information Needed]

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	[More Information Needed]

	#### Annotation process

	[More Information Needed]

	#### Who are the annotators?

	[More Information Needed]

	### 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 is maintained mainly by Yaobo Liang, Yeyun Gong, Nan Duan, Ming Gong, Linjun Shou, and Daniel Campos from Microsoft Research.

	### Licensing Information

	The licensing status of the dataset hinges on the legal status of [XGLUE](https://microsoft.github.io/XGLUE/) hich is unclear.

	### Citation Information

	```
	@article{Liang2020XGLUEAN,
	title={XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation},
	author={Yaobo Liang and Nan Duan and Yeyun Gong and Ning Wu and Fenfei Guo and Weizhen Qi and Ming Gong and Linjun Shou and Daxin Jiang and Guihong Cao and Xiaodong Fan and Ruofei Zhang and Rahul Agrawal and Edward Cui and Sining Wei and Taroon Bharti and Ying Qiao and Jiun-Hung Chen and Winnie Wu and Shuguang Liu and Fan Yang and Daniel Campos and Rangan Majumder and Ming Zhou},
	journal={arXiv},
	year={2020},
	volume={abs/2004.01401}
	}
	```

	### Contributions

	Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.