gem_id
stringlengths
18
22
gem_parent_id
stringlengths
18
22
concept_set_id
int32
0
32.7k
concepts
list
target
stringlengths
16
142
references
list
common_gen-train-0
common_gen-train-0
0
[ "mountain", "ski", "skier" ]
Skier skis down the mountain
[]
common_gen-train-1
common_gen-train-1
0
[ "mountain", "ski", "skier" ]
A skier is skiing down a mountain.
[]
common_gen-train-2
common_gen-train-2
0
[ "mountain", "ski", "skier" ]
Three skiers are skiing on a snowy mountain.
[]
common_gen-train-3
common_gen-train-3
1
[ "dog", "tail", "wag" ]
The dog is wagging his tail.
[]
common_gen-train-4
common_gen-train-4
1
[ "dog", "tail", "wag" ]
A dog wags his tail at the boy.
[]
common_gen-train-5
common_gen-train-5
1
[ "dog", "tail", "wag" ]
a dog wags its tail with its heart
[]
common_gen-train-6
common_gen-train-6
2
[ "canoe", "lake", "paddle" ]
woman paddling canoe on a lake
[]
common_gen-train-7
common_gen-train-7
2
[ "canoe", "lake", "paddle" ]
paddle an open canoe along lake .
[]
common_gen-train-8
common_gen-train-8
2
[ "canoe", "lake", "paddle" ]
a man paddles his canoe on the lake.
[]
common_gen-train-9
common_gen-train-9
3
[ "pull", "station", "train" ]
a train pulls into station
[]
common_gen-train-10
common_gen-train-10
3
[ "pull", "station", "train" ]
train pulling in to station .
[]
common_gen-train-11
common_gen-train-11
3
[ "pull", "station", "train" ]
the train pulling into station
[]
common_gen-train-12
common_gen-train-12
4
[ "eat", "hay", "horse" ]
A horse is eating hay.
[]
common_gen-train-13
common_gen-train-13
4
[ "eat", "hay", "horse" ]
The horses are eating hay.
[]
common_gen-train-14
common_gen-train-14
4
[ "eat", "hay", "horse" ]
A horse eats hay in the barn
[]
common_gen-train-15
common_gen-train-15
5
[ "fan", "match", "watch" ]
watch a match with fans
[]
common_gen-train-16
common_gen-train-16
5
[ "fan", "match", "watch" ]
the fans watch the match
[]
common_gen-train-17
common_gen-train-17
5
[ "fan", "match", "watch" ]
a fan watches during the match
[]
common_gen-train-18
common_gen-train-18
6
[ "lake", "mountain", "surround" ]
a lake surrounded by mountains .
[]
common_gen-train-19
common_gen-train-19
6
[ "lake", "mountain", "surround" ]
lake from the surrounding mountains
[]
common_gen-train-20
common_gen-train-20
6
[ "lake", "mountain", "surround" ]
one of the mountain ranges that surrounds lake .
[]
common_gen-train-21
common_gen-train-21
7
[ "dog", "lay", "rug" ]
A dog laying on a rug.
[]
common_gen-train-22
common_gen-train-22
7
[ "dog", "lay", "rug" ]
The dogs laid down on the rug
[]
common_gen-train-23
common_gen-train-23
7
[ "dog", "lay", "rug" ]
Brown dog chews on bone while laying on the rug.
[]
common_gen-train-24
common_gen-train-24
8
[ "hang", "painting", "wall" ]
hanging a painting on a wall at home
[]
common_gen-train-25
common_gen-train-25
8
[ "hang", "painting", "wall" ]
paintings of horses hang on the walls .
[]
common_gen-train-26
common_gen-train-26
8
[ "hang", "painting", "wall" ]
There is only one painting hanging on the wall.
[]
common_gen-train-27
common_gen-train-27
9
[ "carry", "food", "tray" ]
boy carries a tray of food .
[]
common_gen-train-28
common_gen-train-28
9
[ "carry", "food", "tray" ]
people carrying food on trays
[]
common_gen-train-29
common_gen-train-29
9
[ "carry", "food", "tray" ]
The woman is carrying two trays of food.
[]
common_gen-train-30
common_gen-train-30
10
[ "match", "stadium", "watch" ]
soccer fans watches a league match in a stadium
[]
common_gen-train-31
common_gen-train-31
10
[ "match", "stadium", "watch" ]
A stadium full of people watching a tennis match.
[]
common_gen-train-32
common_gen-train-32
10
[ "match", "stadium", "watch" ]
supporters watch the match from a hill outside the stadium
[]
common_gen-train-33
common_gen-train-33
11
[ "cat", "lick", "paw" ]
A cat licks his paws.
[]
common_gen-train-34
common_gen-train-34
11
[ "cat", "lick", "paw" ]
A cat is licking its paw
[]
common_gen-train-35
common_gen-train-35
11
[ "cat", "lick", "paw" ]
the cat licks the pad of his front paw
[]
common_gen-train-36
common_gen-train-36
12
[ "room", "tile", "wall" ]
a bath room with a toilet and tiled walls
[]
common_gen-train-37
common_gen-train-37
12
[ "room", "tile", "wall" ]
Three men tile a wall in a large empty room
[]
common_gen-train-38
common_gen-train-38
12
[ "room", "tile", "wall" ]
A wall mounted urinal in a checker tiled rest room.
[]
common_gen-train-39
common_gen-train-39
13
[ "canoe", "lake", "shore" ]
canoe on a shore of lake .
[]
common_gen-train-40
common_gen-train-40
13
[ "canoe", "lake", "shore" ]
canoe on shore with rainbow across the lake
[]
common_gen-train-41
common_gen-train-41
13
[ "canoe", "lake", "shore" ]
Several canoes parked in the grass on the shore of a lake
[]
common_gen-train-42
common_gen-train-42
14
[ "mountain", "skier", "way" ]
A skier on his way to the mountain.
[]
common_gen-train-43
common_gen-train-43
14
[ "mountain", "skier", "way" ]
skiers make their way down the mountain
[]
common_gen-train-44
common_gen-train-44
14
[ "mountain", "skier", "way" ]
A skier making her way down a snowy mountain.
[]
common_gen-train-45
common_gen-train-45
15
[ "boat", "drive", "lake" ]
driving boat on a lake
[]
common_gen-train-46
common_gen-train-46
15
[ "boat", "drive", "lake" ]
a boat is being driven through a lake
[]
common_gen-train-47
common_gen-train-47
15
[ "boat", "drive", "lake" ]
A fisherman drives his boat on the lake
[]
common_gen-train-48
common_gen-train-48
16
[ "eat", "grass", "horse" ]
A horse is eating grass.
[]
common_gen-train-49
common_gen-train-49
16
[ "eat", "grass", "horse" ]
The horses are eating grass.
[]
common_gen-train-50
common_gen-train-50
16
[ "eat", "grass", "horse" ]
The old horse ate grass all day.
[]
common_gen-train-51
common_gen-train-51
17
[ "come", "track", "train" ]
train coming down the track
[]
common_gen-train-52
common_gen-train-52
17
[ "come", "track", "train" ]
A train is coming along on a track.
[]
common_gen-train-53
common_gen-train-53
17
[ "come", "track", "train" ]
a long train in coming down some tracks
[]
common_gen-train-54
common_gen-train-54
18
[ "move", "track", "train" ]
train moving on the tracks
[]
common_gen-train-55
common_gen-train-55
18
[ "move", "track", "train" ]
A red train is moving down a track
[]
common_gen-train-56
common_gen-train-56
18
[ "move", "track", "train" ]
A train moves slowly on some empty tracks
[]
common_gen-train-57
common_gen-train-57
19
[ "leave", "station", "train" ]
a train leaves the station
[]
common_gen-train-58
common_gen-train-58
19
[ "leave", "station", "train" ]
a train leaving station bound
[]
common_gen-train-59
common_gen-train-59
19
[ "leave", "station", "train" ]
a fast train about to leave station
[]
common_gen-train-60
common_gen-train-60
20
[ "passenger", "station", "train" ]
train and passengers at the station
[]
common_gen-train-61
common_gen-train-61
20
[ "passenger", "station", "train" ]
passengers leaving a train on a station
[]
common_gen-train-62
common_gen-train-62
20
[ "passenger", "station", "train" ]
a train at station with no passengers joining
[]
common_gen-train-63
common_gen-train-63
21
[ "arrive", "station", "train" ]
a train arrives at station
[]
common_gen-train-64
common_gen-train-64
21
[ "arrive", "station", "train" ]
train arriving at the station
[]
common_gen-train-65
common_gen-train-65
21
[ "arrive", "station", "train" ]
subway train arrives in the station
[]
common_gen-train-66
common_gen-train-66
22
[ "sit", "station", "train" ]
a train sits at the station
[]
common_gen-train-67
common_gen-train-67
22
[ "sit", "station", "train" ]
A train that is sitting in a station.
[]
common_gen-train-68
common_gen-train-68
22
[ "sit", "station", "train" ]
A red train sitting at an empty station.
[]
common_gen-train-69
common_gen-train-69
23
[ "horse", "pull", "wagon" ]
a tea of horses pull a wagon
[]
common_gen-train-70
common_gen-train-70
23
[ "horse", "pull", "wagon" ]
horse pulling man on wagon .
[]
common_gen-train-71
common_gen-train-71
23
[ "horse", "pull", "wagon" ]
A wagon is being pulled by horses.
[]
common_gen-train-72
common_gen-train-72
24
[ "station", "stop", "train" ]
train is stopped at a station
[]
common_gen-train-73
common_gen-train-73
24
[ "station", "stop", "train" ]
trains stopping at the station
[]
common_gen-train-74
common_gen-train-74
24
[ "station", "stop", "train" ]
The empty train is stopped in the station.
[]
common_gen-train-75
common_gen-train-75
25
[ "plane", "runway", "sit" ]
A plane sits on the runway
[]
common_gen-train-76
common_gen-train-76
25
[ "plane", "runway", "sit" ]
An old plane is sitting on a runway.
[]
common_gen-train-77
common_gen-train-77
25
[ "plane", "runway", "sit" ]
Two planes are sitting out on the runway.
[]
common_gen-train-78
common_gen-train-78
26
[ "cloud", "fly", "plane" ]
plane flying into the clouds
[]
common_gen-train-79
common_gen-train-79
26
[ "cloud", "fly", "plane" ]
flying plane against a cloud .
[]
common_gen-train-80
common_gen-train-80
26
[ "cloud", "fly", "plane" ]
A plane flies over head in the clouds.
[]
common_gen-train-81
common_gen-train-81
27
[ "dog", "herd", "sheep" ]
A dog herds a sheep.
[]
common_gen-train-82
common_gen-train-82
27
[ "dog", "herd", "sheep" ]
A dog is herding sheep.
[]
common_gen-train-83
common_gen-train-83
27
[ "dog", "herd", "sheep" ]
The dogs are herding sheep.
[]
common_gen-train-84
common_gen-train-84
28
[ "beach", "boat", "sit" ]
boats sitting on the beach
[]
common_gen-train-85
common_gen-train-85
28
[ "beach", "boat", "sit" ]
a boat is sitting up on a beach
[]
common_gen-train-86
common_gen-train-86
28
[ "beach", "boat", "sit" ]
Pelicans sit on a blue boat at the beach.
[]
common_gen-train-87
common_gen-train-87
29
[ "come", "station", "train" ]
a train coming into station
[]
common_gen-train-88
common_gen-train-88
29
[ "come", "station", "train" ]
tube train comes to station .
[]
common_gen-train-89
common_gen-train-89
29
[ "come", "station", "train" ]
train coming in to the station
[]
common_gen-train-90
common_gen-train-90
30
[ "cloud", "float", "sky" ]
clouds floating in the sky
[]
common_gen-train-91
common_gen-train-91
30
[ "cloud", "float", "sky" ]
clouds float through a blue sky
[]
common_gen-train-92
common_gen-train-92
30
[ "cloud", "float", "sky" ]
shot of clouds that float across the sky
[]
common_gen-train-93
common_gen-train-93
31
[ "eat", "elephant", "grass" ]
elephants pulling grass to eat .
[]
common_gen-train-94
common_gen-train-94
31
[ "eat", "elephant", "grass" ]
An elephant is eating grass in Kenya.
[]
common_gen-train-95
common_gen-train-95
31
[ "eat", "elephant", "grass" ]
a bunch of elephants are eating grass
[]
common_gen-train-96
common_gen-train-96
32
[ "family", "spend", "time" ]
family spend time in the park
[]
common_gen-train-97
common_gen-train-97
32
[ "family", "spend", "time" ]
spending time with the family
[]
common_gen-train-98
common_gen-train-98
32
[ "family", "spend", "time" ]
family spend time at a holidays
[]
common_gen-train-99
common_gen-train-99
33
[ "bathroom", "tile", "wall" ]
black walls and tiles in the bathroom
[]

Dataset Card for GEM

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:

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

Languages

More Information Needed

Dataset Structure

Data Instances

common_gen

  • Size of downloaded dataset files: 1.85 MB
  • Size of the generated dataset: 9.23 MB
  • Total amount of disk used: 11.07 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: 1.47 MB
  • Size of the generated dataset: 1.31 MB
  • Total amount of disk used: 2.77 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: 29.37 MB
  • Size of the generated dataset: 27.44 MB
  • Total amount of disk used: 56.81 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: 14.60 MB
  • Size of the generated dataset: 12.14 MB
  • Total amount of disk used: 26.74 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: 347.36 MB
  • Size of the generated dataset: 951.06 MB
  • Total amount of disk used: 1.30 GB

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: 514.11 MB
  • Size of the generated dataset: 1.31 GB
  • Total amount of disk used: 1.83 GB

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: 8.64 MB
  • Size of the generated dataset: 45.78 MB
  • Total amount of disk used: 54.43 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: 187.73 MB
  • Size of the generated dataset: 757.99 MB
  • Total amount of disk used: 945.72 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: 12.95 MB
  • Size of the generated dataset: 14.63 MB
  • Total amount of disk used: 27.57 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: 7.63 MB
  • Size of the generated dataset: 8.41 MB
  • Total amount of disk used: 16.04 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: 127.27 MB
  • Size of the generated dataset: 152.77 MB
  • Total amount of disk used: 280.04 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: 169.41 MB
  • Size of the generated dataset: 287.60 MB
  • Total amount of disk used: 457.01 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: 169.41 MB
  • Size of the generated dataset: 211.21 MB
  • Total amount of disk used: 380.62 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: 169.41 MB
  • Size of the generated dataset: 10.35 MB
  • Total amount of disk used: 179.75 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: 169.41 MB
  • Size of the generated dataset: 41.02 MB
  • Total amount of disk used: 210.43 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: 254.89 MB
  • Size of the generated dataset: 70.67 MB
  • Total amount of disk used: 325.56 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

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

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

More Information Needed

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 for adding this dataset.

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