Datasets:

GEM
/

gem

	---
	annotations_creators:
	common_gen:
	- crowdsourced
	- found
	cs_restaurants:
	- crowdsourced
	- found
	dart:
	- crowdsourced
	- found
	e2e_nlg:
	- crowdsourced
	- found
	mlsum_de:
	- found
	mlsum_es:
	- found
	schema_guided_dialog:
	- crowdsourced
	totto:
	- crowdsourced
	- found
	web_nlg_en:
	- crowdsourced
	- found
	web_nlg_ru:
	- crowdsourced
	- found
	wiki_auto_asset_turk:
	- crowdsourced
	- found
	wiki_lingua_es_en:
	- found
	wiki_lingua_ru_en:
	- found
	wiki_lingua_tr_en:
	- found
	wiki_lingua_vi_en:
	- found
	xsum:
	- found
	language_creators:
	common_gen:
	- found
	cs_restaurants:
	- found
	dart:
	- found
	e2e_nlg:
	- found
	mlsum_de:
	- found
	mlsum_es:
	- found
	schema_guided_dialog:
	- crowdsourced
	- machine-generated
	totto:
	- found
	web_nlg_en:
	- found
	web_nlg_ru:
	- found
	wiki_auto_asset_turk:
	- found
	wiki_lingua_es_en:
	- found
	wiki_lingua_ru_en:
	- found
	wiki_lingua_tr_en:
	- found
	wiki_lingua_vi_en:
	- found
	xsum:
	- found
	languages:
	common_gen:
	- en
	cs_restaurants:
	- cs
	dart:
	- en
	e2e_nlg:
	- en
	mlsum_de:
	- de
	mlsum_es:
	- es
	schema_guided_dialog:
	- en
	totto:
	- en
	web_nlg_en:
	- en
	web_nlg_ru:
	- ru
	wiki_auto_asset_turk:
	- en
	wiki_lingua_es_en:
	- en
	- es
	wiki_lingua_ru_en:
	- en
	- ru
	wiki_lingua_tr_en:
	- en
	- tr
	wiki_lingua_vi_en:
	- en
	- vi
	xsum:
	- en
	licenses:
	- other-research-only
	multilinguality:
	common_gen:
	- monolingual
	cs_restaurants:
	- monolingual
	dart:
	- monolingual
	e2e_nlg:
	- monolingual
	mlsum_de:
	- monolingual
	mlsum_es:
	- monolingual
	schema_guided_dialog:
	- monolingual
	totto:
	- monolingual
	web_nlg_en:
	- monolingual
	web_nlg_ru:
	- monolingual
	wiki_auto_asset_turk:
	- monolingual
	wiki_lingua_es_en:
	- multilingual
	wiki_lingua_ru_en:
	- multilingual
	wiki_lingua_tr_en:
	- multilingual
	wiki_lingua_vi_en:
	- multilingual
	xsum:
	- monolingual
	size_categories:
	common_gen:
	- 10K<n<100K
	cs_restaurants:
	- 1K<n<10K
	dart:
	- 10K<n<100K
	e2e_nlg:
	- 10K<n<100K
	mlsum_de:
	- 100K<n<1M
	mlsum_es:
	- 100K<n<1M
	schema_guided_dialog:
	- 100K<n<1M
	totto:
	- 100K<n<1M
	web_nlg_en:
	- 10K<n<100K
	web_nlg_ru:
	- 10K<n<100K
	wiki_auto_asset_turk:
	- 100K<n<1M
	wiki_lingua_es_en:
	- 100K<n<1M
	wiki_lingua_ru_en:
	- 10K<n<100K
	wiki_lingua_tr_en:
	- 1K<n<10K
	wiki_lingua_vi_en:
	- 10K<n<100K
	xsum:
	- 10K<n<100K
	source_datasets:
	common_gen:
	- extended\|other-vision-datasets
	- original
	cs_restaurants:
	- original
	dart:
	- original
	e2e_nlg:
	- original
	mlsum_de:
	- original
	mlsum_es:
	- original
	schema_guided_dialog:
	- original
	totto:
	- original
	web_nlg_en:
	- original
	web_nlg_ru:
	- original
	wiki_auto_asset_turk:
	- original
	wiki_lingua_es_en:
	- original
	wiki_lingua_ru_en:
	- original
	wiki_lingua_tr_en:
	- original
	wiki_lingua_vi_en:
	- original
	xsum:
	- original
	task_categories:
	common_gen:
	- conditional-text-generation
	cs_restaurants:
	- conditional-text-generation
	dart:
	- conditional-text-generation
	e2e_nlg:
	- conditional-text-generation
	mlsum_de:
	- conditional-text-generation
	mlsum_es:
	- conditional-text-generation
	schema_guided_dialog:
	- sequence-modeling
	totto:
	- conditional-text-generation
	web_nlg_en:
	- conditional-text-generation
	web_nlg_ru:
	- conditional-text-generation
	wiki_auto_asset_turk:
	- conditional-text-generation
	wiki_lingua_es_en:
	- conditional-text-generation
	wiki_lingua_ru_en:
	- conditional-text-generation
	wiki_lingua_tr_en:
	- conditional-text-generation
	wiki_lingua_vi_en:
	- conditional-text-generation
	xsum:
	- conditional-text-generation
	task_ids:
	common_gen:
	- other-structured-to-text
	cs_restaurants:
	- other-structured-to-text
	dart:
	- other-structured-to-text
	e2e_nlg:
	- other-structured-to-text
	mlsum_de:
	- summarization
	mlsum_es:
	- summarization
	schema_guided_dialog:
	- dialogue-modeling
	totto:
	- table-to-text
	web_nlg_en:
	- other-structured-to-text
	web_nlg_ru:
	- other-structured-to-text
	wiki_auto_asset_turk:
	- text-simplification
	wiki_lingua_es_en:
	- summarization
	wiki_lingua_ru_en:
	- summarization
	wiki_lingua_tr_en:
	- summarization
	wiki_lingua_vi_en:
	- summarization
	xsum:
	- summarization
	paperswithcode_id: gem
	pretty_name: GEM
	---

	# Dataset Card for GEM

	## 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: [https://gem-benchmark.github.io/](https://gem-benchmark.github.io/)
	- Repository:
	- Paper: [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://arxiv.org/abs/2102.01672)
	- Point of Contact: [Sebastian Gehrman](gehrmann@google.com)
	- Size of downloaded dataset files: 2084.23 MB
	- Size of the generated dataset: 3734.73 MB
	- Total amount of disk used: 5818.96 MB

	### Dataset Summary

	GEM is a benchmark environment for Natural Language Generation with a focus on its Evaluation,
	both through human annotations and automated Metrics.

	GEM aims to:
	- measure NLG progress across 13 datasets spanning many NLG tasks and languages.
	- provide an in-depth analysis of data and models presented via data statements and challenge sets.
	- develop standards for evaluation of generated text using both automated and human metrics.

	It is our goal to regularly update GEM and to encourage toward more inclusive practices in dataset development
	by extending existing data or developing datasets for additional languages.

	You can find more complete information in the dataset cards for each of the subsets:
	- [CommonGen](https://gem-benchmark.github.io/data_cards/CommonGen)
	- [Czech Restaurant](https://gem-benchmark.github.io/data_cards/Czech%20Restaurant)
	- [DART](https://gem-benchmark.github.io/data_cards/DART)
	- [E2E](https://gem-benchmark.github.io/data_cards/E2E)
	- [MLSum](https://gem-benchmark.github.io/data_cards/MLSum)
	- [Schema-Guided Dialog](https://gem-benchmark.github.io/data_cards/Schema-Guided%20DIalog)
	- [WebNLG](https://gem-benchmark.github.io/data_cards/WebNLG)
	- [Wiki-Auto](https://gem-benchmark.github.io/data_cards/Wiki-Auto)/[ASSET](https://gem-benchmark.github.io/data_cards/ASSET)/[TURK](https://gem-benchmark.github.io/data_cards/TURK)
	- [WikiLingua](https://gem-benchmark.github.io/data_cards/WikiLingua)
	- [XSum](https://gem-benchmark.github.io/data_cards/XSum)

	The subsets are organized by task:
	```
	{
	"summarization": {
	"mlsum": ["mlsum_de", "mlsum_es"],
	"wiki_lingua": ["wiki_lingua_es_en", "wiki_lingua_ru_en", "wiki_lingua_tr_en", "wiki_lingua_vi_en"],
	"xsum": ["xsum"],
	},
	"struct2text": {
	"common_gen": ["common_gen"],
	"cs_restaurants": ["cs_restaurants"],
	"dart": ["dart"],
	"e2e": ["e2e_nlg"],
	"totto": ["totto"],
	"web_nlg": ["web_nlg_en", "web_nlg_ru"],
	},
	"simplification": {
	"wiki_auto_asset_turk": ["wiki_auto_asset_turk"],
	},
	"dialog": {
	"schema_guided_dialog": ["schema_guided_dialog"],
	},
	}
	```

	Each example has one `target` per example in its training set, and a set of `references` (with one or more items) in its validation and test set.

	### Supported Tasks and Leaderboards

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Languages

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Dataset Structure

	We show detailed information for up to 5 configurations of the dataset.

	### Data Instances

	#### common_gen

	- Size of downloaded dataset files: 1.76 MB
	- Size of the generated dataset: 8.80 MB
	- Total amount of disk used: 10.56 MB

	An example of `validation` looks as follows.
	```
	{'concept_set_id': 0,
	'concepts': ['field', 'look', 'stand'],
	'gem_id': 'common_gen-validation-0',
	'references': ['The player stood in the field looking at the batter.',
	'The coach stands along the field, looking at the goalkeeper.',
	'I stood and looked across the field, peacefully.',
	'Someone stands, looking around the empty field.'],
	'target': 'The player stood in the field looking at the batter.'}
	```

	#### cs_restaurants

	- Size of downloaded dataset files: 1.40 MB
	- Size of the generated dataset: 1.25 MB
	- Total amount of disk used: 2.64 MB

	An example of `validation` looks as follows.
	```
	{'dialog_act': '?request(area)',
	'dialog_act_delexicalized': '?request(area)',
	'gem_id': 'cs_restaurants-validation-0',
	'references': ['Jakou lokalitu hledáte ?'],
	'target': 'Jakou lokalitu hledáte ?',
	'target_delexicalized': 'Jakou lokalitu hledáte ?'}
	```

	#### dart

	- Size of downloaded dataset files: 28.01 MB
	- Size of the generated dataset: 26.17 MB
	- Total amount of disk used: 54.18 MB

	An example of `validation` looks as follows.
	```
	{'dart_id': 0,
	'gem_id': 'dart-validation-0',
	'references': ['A school from Mars Hill, North Carolina, joined in 1973.'],
	'subtree_was_extended': True,
	'target': 'A school from Mars Hill, North Carolina, joined in 1973.',
	'target_sources': ['WikiSQL_decl_sents'],
	'tripleset': [['Mars Hill College', 'JOINED', '1973'], ['Mars Hill College', 'LOCATION', 'Mars Hill, North Carolina']]}
	```

	#### e2e_nlg

	- Size of downloaded dataset files: 13.92 MB
	- Size of the generated dataset: 11.58 MB
	- Total amount of disk used: 25.50 MB

	An example of `validation` looks as follows.
	```
	{'gem_id': 'e2e_nlg-validation-0',
	'meaning_representation': 'name[Alimentum], area[city centre], familyFriendly[no]',
	'references': ['There is a place in the city centre, Alimentum, that is not family-friendly.'],
	'target': 'There is a place in the city centre, Alimentum, that is not family-friendly.'}
	```

	#### mlsum_de

	- Size of downloaded dataset files: 331.27 MB
	- Size of the generated dataset: 907.00 MB
	- Total amount of disk used: 1238.27 MB

	An example of `validation` looks as follows.
	```
	{'date': '00/04/2019',
	'gem_id': 'mlsum_de-validation-0',
	'references': ['In einer Kleinstadt auf der Insel Usedom war eine junge Frau tot in ihrer Wohnung gefunden worden. Nun stehen zwei Bekannte unter Verdacht.'],
	'target': 'In einer Kleinstadt auf der Insel Usedom war eine junge Frau tot in ihrer Wohnung gefunden worden. Nun stehen zwei Bekannte unter Verdacht.',
	'text': 'Kerzen und Blumen stehen vor dem Eingang eines Hauses, in dem eine 18-jährige Frau tot aufgefunden wurde. In einer Kleinstadt auf der Insel Usedom war eine junge Frau tot in ...',
	'title': 'Tod von 18-Jähriger auf Usedom: Zwei Festnahmen',
	'topic': 'panorama',
	'url': 'https://www.sueddeutsche.de/panorama/usedom-frau-tot-festnahme-verdaechtige-1.4412256'}
	```

	#### mlsum_es

	- Size of downloaded dataset files: 490.29 MB
	- Size of the generated dataset: 1253.63 MB
	- Total amount of disk used: 1743.92 MB

	An example of `validation` looks as follows.
	```
	{'date': '05/01/2019',
	'gem_id': 'mlsum_es-validation-0',
	'references': ['El diseñador que dio carta de naturaleza al estilo genuinamente americano celebra el medio siglo de su marca entre grandes fastos y problemas financieros. Conectar con las nuevas generaciones es el regalo que precisa más que nunca'],
	'target': 'El diseñador que dio carta de naturaleza al estilo genuinamente americano celebra el medio siglo de su marca entre grandes fastos y problemas financieros. Conectar con las nuevas generaciones es el regalo que precisa más que nunca',
	'text': 'Un oso de peluche marcándose un heelflip de monopatín es todo lo que Ralph Lauren necesitaba esta Navidad. Estampado en un jersey de lana azul marino, supone la guinda que corona ...',
	'title': 'Ralph Lauren busca el secreto de la eterna juventud',
	'topic': 'elpais estilo',
	'url': 'http://elpais.com/elpais/2019/01/04/estilo/1546617396_933318.html'}
	```

	#### schema_guided_dialog

	- Size of downloaded dataset files: 8.24 MB
	- Size of the generated dataset: 43.66 MB
	- Total amount of disk used: 51.91 MB

	An example of `validation` looks as follows.
	```
	{'dialog_acts': [{'act': 2, 'slot': 'song_name', 'values': ['Carnivore']}, {'act': 2, 'slot': 'playback_device', 'values': ['TV']}],
	'dialog_id': '10_00054',
	'gem_id': 'schema_guided_dialog-validation-0',
	'prompt': 'Yes, I would.',
	'references': ['Please confirm the song Carnivore on tv.'],
	'target': 'Please confirm the song Carnivore on tv.',
	'turn_id': 15}
	```

	#### totto

	- Size of downloaded dataset files: 179.03 MB
	- Size of the generated dataset: 722.88 MB
	- Total amount of disk used: 901.91 MB

	An example of `validation` looks as follows.
	```
	{'example_id': '7391450717765563190',
	'gem_id': 'totto-validation-0',
	'highlighted_cells': [[3, 0], [3, 2], [3, 3]],
	'overlap_subset': 'True',
	'references': ['Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.',
	'Daniel Henry Chamberlain was the 76th Governor of South Carolina, beginning in 1874.',
	'Daniel Henry Chamberlain was the 76th Governor of South Carolina who took office in 1874.'],
	'sentence_annotations': [{'final_sentence': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.',
	'original_sentence': 'Daniel Henry Chamberlain (June 23, 1835 – April 13, 1907) was an American planter, lawyer, author and the 76th Governor of South Carolina '
	'from 1874 until 1877.',
	'sentence_after_ambiguity': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.',
	'sentence_after_deletion': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.'},
	...
	],
	'table': [[{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': '#'},
	{'column_span': 2, 'is_header': True, 'row_span': 1, 'value': 'Governor'},
	{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Took Office'},
	{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Left Office'}],
	[{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': '74'},
	{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '-'},
	{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Robert Kingston Scott'},
	{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'July 6, 1868'}],
	...
	],
	'table_page_title': 'List of Governors of South Carolina',
	'table_section_text': 'Parties Democratic Republican',
	'table_section_title': 'Governors under the Constitution of 1868',
	'table_webpage_url': 'http://en.wikipedia.org/wiki/List_of_Governors_of_South_Carolina',
	'target': 'Daniel Henry Chamberlain was the 76th Governor of South Carolina from 1874.',
	'totto_id': 0}
	```

	#### web_nlg_en

	- Size of downloaded dataset files: 12.35 MB
	- Size of the generated dataset: 13.95 MB
	- Total amount of disk used: 26.29 MB

	An example of `validation` looks as follows.
	```
	{'category': 'Airport',
	'gem_id': 'web_nlg_en-validation-0',
	'input': ['Aarhus \| leader \| Jacob_Bundsgaard'],
	'references': ['The leader of Aarhus is Jacob Bundsgaard.'],
	'target': 'The leader of Aarhus is Jacob Bundsgaard.',
	'webnlg_id': 'dev/Airport/1/Id1'}
	```

	#### web_nlg_ru

	- Size of downloaded dataset files: 7.28 MB
	- Size of the generated dataset: 8.02 MB
	- Total amount of disk used: 15.30 MB

	An example of `validation` looks as follows.
	```
	{'category': 'Airport',
	'gem_id': 'web_nlg_ru-validation-0',
	'input': ['Punjab,_Pakistan \| leaderTitle \| Provincial_Assembly_of_the_Punjab'],
	'references': ['Пенджаб, Пакистан, возглавляется Провинциальной ассамблеей Пенджаба.', 'Пенджаб, Пакистан возглавляется Провинциальной ассамблеей Пенджаба.'],
	'target': 'Пенджаб, Пакистан, возглавляется Провинциальной ассамблеей Пенджаба.',
	'webnlg_id': 'dev/Airport/1/Id1'}
	```

	#### wiki_auto_asset_turk

	- Size of downloaded dataset files: 121.37 MB
	- Size of the generated dataset: 145.69 MB
	- Total amount of disk used: 267.07 MB

	An example of `validation` looks as follows.
	```
	{'gem_id': 'wiki_auto_asset_turk-validation-0',
	'references': ['The Gandalf Awards honor excellent writing in in fantasy literature.'],
	'source': 'The Gandalf Awards, honoring achievement in fantasy literature, were conferred by the World Science Fiction Society annually from 1974 to 1981.',
	'source_id': '350_691837-1-0-0',
	'target': 'The Gandalf Awards honor excellent writing in in fantasy literature.',
	'target_id': '350_691837-0-0-0'}
	```

	#### wiki_lingua_es_en

	- Size of downloaded dataset files: 161.56 MB
	- Size of the generated dataset: 274.28 MB
	- Total amount of disk used: 435.84 MB

	An example of `validation` looks as follows.
	```
	'references': ["Practice matted hair prevention from early in your cat's life. Make sure that your cat is grooming itself effectively. Keep a close eye on cats with long hair."],
	'source': 'Muchas personas presentan problemas porque no cepillaron el pelaje de sus gatos en una etapa temprana de su vida, ya que no lo consideraban necesario. Sin embargo, a medida que...',
	'target': "Practice matted hair prevention from early in your cat's life. Make sure that your cat is grooming itself effectively. Keep a close eye on cats with long hair."}
	```

	#### wiki_lingua_ru_en

	- Size of downloaded dataset files: 161.56 MB
	- Size of the generated dataset: 201.43 MB
	- Total amount of disk used: 362.99 MB

	An example of `validation` looks as follows.
	```
	{'gem_id': 'wiki_lingua_ru_en-val-0',
	'references': ['Get immediate medical care if you notice signs of a complication. Undergo diagnostic tests to check for gallstones and complications. Ask your doctor about your treatment '
	'options.'],
	'source': 'И хотя, скорее всего, вам не о чем волноваться, следует незамедлительно обратиться к врачу, если вы подозреваете, что у вас возникло осложнение желчекаменной болезни. Это ...',
	'target': 'Get immediate medical care if you notice signs of a complication. Undergo diagnostic tests to check for gallstones and complications. Ask your doctor about your treatment '
	'options.'}
	```

	#### wiki_lingua_tr_en

	- Size of downloaded dataset files: 161.56 MB
	- Size of the generated dataset: 9.87 MB
	- Total amount of disk used: 171.42 MB

	An example of `validation` looks as follows.
	```
	{'gem_id': 'wiki_lingua_tr_en-val-0',
	'references': ['Open Instagram. Go to the video you want to download. Tap ⋮. Tap Copy Link. Open Google Chrome. Tap the address bar. Go to the SaveFromWeb site. Tap the "Paste Instagram Video" text box. Tap and hold the text box. Tap PASTE. Tap Download. Download the video. Find the video on your Android.'],
	'source': 'Instagram uygulamasının çok renkli kamera şeklindeki simgesine dokun. Daha önce giriş yaptıysan Instagram haber kaynağı açılır. Giriş yapmadıysan istendiğinde e-posta adresini ...',
	'target': 'Open Instagram. Go to the video you want to download. Tap ⋮. Tap Copy Link. Open Google Chrome. Tap the address bar. Go to the SaveFromWeb site. Tap the "Paste Instagram Video" text box. Tap and hold the text box. Tap PASTE. Tap Download. Download the video. Find the video on your Android.'}
	```

	#### wiki_lingua_vi_en

	- Size of downloaded dataset files: 161.56 MB
	- Size of the generated dataset: 39.12 MB
	- Total amount of disk used: 200.68 MB

	An example of `validation` looks as follows.
	```
	{'gem_id': 'wiki_lingua_vi_en-val-0',
	'references': ['Select the right time of year for planting the tree. You will usually want to plant your tree when it is dormant, or not flowering, during cooler or colder times of year.'],
	'source': 'Bạn muốn cung cấp cho cây cơ hội tốt nhất để phát triển và sinh tồn. Trồng cây đúng thời điểm trong năm chính là yếu tố then chốt. Thời điểm sẽ thay đổi phụ thuộc vào loài cây ...',
	'target': 'Select the right time of year for planting the tree. You will usually want to plant your tree when it is dormant, or not flowering, during cooler or colder times of year.'}
	```

	#### xsum

	- Size of downloaded dataset files: 243.08 MB
	- Size of the generated dataset: 67.40 MB
	- Total amount of disk used: 310.48 MB

	An example of `validation` looks as follows.
	```
	{'document': 'Burberry reported pre-tax profits of £166m for the year to March. A year ago it made a loss of £16.1m, hit by charges at its Spanish operations.\n'
	'In the past year it has opened 21 new stores and closed nine. It plans to open 20-30 stores this year worldwide.\n'
	'The group has also focused on promoting the Burberry brand online...',
	'gem_id': 'xsum-validation-0',
	'references': ['Luxury fashion designer Burberry has returned to profit after opening new stores and spending more on online marketing'],
	'target': 'Luxury fashion designer Burberry has returned to profit after opening new stores and spending more on online marketing',
	'xsum_id': '10162122'}
	```

	### Data Fields

	The data fields are the same among all splits.

	#### common_gen
	- `gem_id`: a `string` feature.
	- `concept_set_id`: a `int32` feature.
	- `concepts`: a `list` of `string` features.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### cs_restaurants
	- `gem_id`: a `string` feature.
	- `dialog_act`: a `string` feature.
	- `dialog_act_delexicalized`: a `string` feature.
	- `target_delexicalized`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### dart
	- `gem_id`: a `string` feature.
	- `dart_id`: a `int32` feature.
	- `tripleset`: a `list` of `string` features.
	- `subtree_was_extended`: a `bool` feature.
	- `target_sources`: a `list` of `string` features.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### e2e_nlg
	- `gem_id`: a `string` feature.
	- `meaning_representation`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### mlsum_de
	- `gem_id`: a `string` feature.
	- `text`: a `string` feature.
	- `topic`: a `string` feature.
	- `url`: a `string` feature.
	- `title`: a `string` feature.
	- `date`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### mlsum_es
	- `gem_id`: a `string` feature.
	- `text`: a `string` feature.
	- `topic`: a `string` feature.
	- `url`: a `string` feature.
	- `title`: a `string` feature.
	- `date`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### schema_guided_dialog
	- `gem_id`: a `string` feature.
	- `act`: a classification label, with possible values including `AFFIRM` (0), `AFFIRM_INTENT` (1), `CONFIRM` (2), `GOODBYE` (3), `INFORM` (4).
	- `slot`: a `string` feature.
	- `values`: a `list` of `string` features.
	- `dialog_id`: a `string` feature.
	- `turn_id`: a `int32` feature.
	- `prompt`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### totto
	- `gem_id`: a `string` feature.
	- `totto_id`: a `int32` feature.
	- `table_page_title`: a `string` feature.
	- `table_webpage_url`: a `string` feature.
	- `table_section_title`: a `string` feature.
	- `table_section_text`: a `string` feature.
	- `column_span`: a `int32` feature.
	- `is_header`: a `bool` feature.
	- `row_span`: a `int32` feature.
	- `value`: a `string` feature.
	- `highlighted_cells`: a `list` of `int32` features.
	- `example_id`: a `string` feature.
	- `original_sentence`: a `string` feature.
	- `sentence_after_deletion`: a `string` feature.
	- `sentence_after_ambiguity`: a `string` feature.
	- `final_sentence`: a `string` feature.
	- `overlap_subset`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### web_nlg_en
	- `gem_id`: a `string` feature.
	- `input`: a `list` of `string` features.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.
	- `category`: a `string` feature.
	- `webnlg_id`: a `string` feature.

	#### web_nlg_ru
	- `gem_id`: a `string` feature.
	- `input`: a `list` of `string` features.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.
	- `category`: a `string` feature.
	- `webnlg_id`: a `string` feature.

	#### wiki_auto_asset_turk
	- `gem_id`: a `string` feature.
	- `source_id`: a `string` feature.
	- `target_id`: a `string` feature.
	- `source`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### wiki_lingua_es_en
	- `gem_id`: a `string` feature.
	- `source`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### wiki_lingua_ru_en
	- `gem_id`: a `string` feature.
	- `source`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### wiki_lingua_tr_en
	- `gem_id`: a `string` feature.
	- `source`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### wiki_lingua_vi_en
	- `gem_id`: a `string` feature.
	- `source`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	#### xsum
	- `gem_id`: a `string` feature.
	- `xsum_id`: a `string` feature.
	- `document`: a `string` feature.
	- `target`: a `string` feature.
	- `references`: a `list` of `string` features.

	### Data Splits

	#### common_gen

	\| \|train\|validation\|test\|
	\|----------\|----:\|---------:\|---:\|
	\|common_gen\|67389\| 993\|1497\|

	#### cs_restaurants

	\| \|train\|validation\|test\|
	\|--------------\|----:\|---------:\|---:\|
	\|cs_restaurants\| 3569\| 781\| 842\|

	#### dart

	\| \|train\|validation\|test\|
	\|----\|----:\|---------:\|---:\|
	\|dart\|62659\| 2768\|6959\|

	#### e2e_nlg

	\| \|train\|validation\|test\|
	\|-------\|----:\|---------:\|---:\|
	\|e2e_nlg\|33525\| 4299\|4693\|

	#### mlsum_de

	\| \|train \|validation\|test \|
	\|--------\|-----:\|---------:\|----:\|
	\|mlsum_de\|220748\| 11392\|10695\|

	#### mlsum_es

	\| \|train \|validation\|test \|
	\|--------\|-----:\|---------:\|----:\|
	\|mlsum_es\|259886\| 9977\|13365\|

	#### schema_guided_dialog

	\| \|train \|validation\|test \|
	\|--------------------\|-----:\|---------:\|----:\|
	\|schema_guided_dialog\|164982\| 10000\|10000\|

	#### totto

	\| \|train \|validation\|test\|
	\|-----\|-----:\|---------:\|---:\|
	\|totto\|121153\| 7700\|7700\|

	#### web_nlg_en

	\| \|train\|validation\|test\|
	\|----------\|----:\|---------:\|---:\|
	\|web_nlg_en\|35426\| 1667\|1779\|

	#### web_nlg_ru

	\| \|train\|validation\|test\|
	\|----------\|----:\|---------:\|---:\|
	\|web_nlg_ru\|14630\| 790\|1102\|

	#### wiki_auto_asset_turk

	\| \|train \|validation\|test_asset\|test_turk\|
	\|--------------------\|-----:\|---------:\|---------:\|--------:\|
	\|wiki_auto_asset_turk\|373801\| 73249\| 359\| 359\|

	#### wiki_lingua_es_en

	\| \|train\|validation\|test \|
	\|-----------------\|----:\|---------:\|----:\|
	\|wiki_lingua_es_en\|79515\| 8835\|19797\|

	#### wiki_lingua_ru_en

	\| \|train\|validation\|test\|
	\|-----------------\|----:\|---------:\|---:\|
	\|wiki_lingua_ru_en\|36898\| 4100\|9094\|

	#### wiki_lingua_tr_en

	\| \|train\|validation\|test\|
	\|-----------------\|----:\|---------:\|---:\|
	\|wiki_lingua_tr_en\| 3193\| 355\| 808\|

	#### wiki_lingua_vi_en

	\| \|train\|validation\|test\|
	\|-----------------\|----:\|---------:\|---:\|
	\|wiki_lingua_vi_en\| 9206\| 1023\|2167\|

	#### xsum

	\| \|train\|validation\|test\|
	\|----\|----:\|---------:\|---:\|
	\|xsum\|23206\| 1117\|1166\|

	## Dataset Creation

	### Curation Rationale

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the source language producers?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Annotations

	#### Annotation process

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the annotators?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Personal and Sensitive Information

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Discussion of Biases

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Other Known Limitations

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Additional Information

	### Dataset Curators

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Licensing Information

	CC-BY-SA-4.0

	### Citation Information

	```
	@article{gem_benchmark,
	author = {Sebastian Gehrmann and
	Tosin P. Adewumi and
	Karmanya Aggarwal and
	Pawan Sasanka Ammanamanchi and
	Aremu Anuoluwapo and
	Antoine Bosselut and
	Khyathi Raghavi Chandu and
	Miruna{-}Adriana Clinciu and
	Dipanjan Das and
	Kaustubh D. Dhole and
	Wanyu Du and
	Esin Durmus and
	Ondrej Dusek and
	Chris Emezue and
	Varun Gangal and
	Cristina Garbacea and
	Tatsunori Hashimoto and
	Yufang Hou and
	Yacine Jernite and
	Harsh Jhamtani and
	Yangfeng Ji and
	Shailza Jolly and
	Dhruv Kumar and
	Faisal Ladhak and
	Aman Madaan and
	Mounica Maddela and
	Khyati Mahajan and
	Saad Mahamood and
	Bodhisattwa Prasad Majumder and
	Pedro Henrique Martins and
	Angelina McMillan{-}Major and
	Simon Mille and
	Emiel van Miltenburg and
	Moin Nadeem and
	Shashi Narayan and
	Vitaly Nikolaev and
	Rubungo Andre Niyongabo and
	Salomey Osei and
	Ankur P. Parikh and
	Laura Perez{-}Beltrachini and
	Niranjan Ramesh Rao and
	Vikas Raunak and
	Juan Diego Rodriguez and
	Sashank Santhanam and
	Jo{\~{a}}o Sedoc and
	Thibault Sellam and
	Samira Shaikh and
	Anastasia Shimorina and
	Marco Antonio Sobrevilla Cabezudo and
	Hendrik Strobelt and
	Nishant Subramani and
	Wei Xu and
	Diyi Yang and
	Akhila Yerukola and
	Jiawei Zhou},
	title = {The {GEM} Benchmark: Natural Language Generation, its Evaluation and
	Metrics},
	journal = {CoRR},
	volume = {abs/2102.01672},
	year = {2021},
	url = {https://arxiv.org/abs/2102.01672},
	archivePrefix = {arXiv},
	eprint = {2102.01672}
	}
	```

	### Contributions

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