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concept_set_idx (int)concepts (json)target (string)
0
[ "ski", "mountain", "skier" ]
Skier skis down the mountain
0
[ "ski", "mountain", "skier" ]
A skier is skiing down a mountain.
0
[ "ski", "mountain", "skier" ]
Three skiers are skiing on a snowy mountain.
1
[ "wag", "tail", "dog" ]
The dog is wagging his tail.
1
[ "wag", "tail", "dog" ]
A dog wags his tail at the boy.
1
[ "wag", "tail", "dog" ]
a dog wags its tail with its heart
2
[ "lake", "paddle", "canoe" ]
woman paddling canoe on a lake
2
[ "lake", "paddle", "canoe" ]
paddle an open canoe along lake .
2
[ "lake", "paddle", "canoe" ]
a man paddles his canoe on the lake.
3
[ "station", "train", "pull" ]
a train pulls into station
3
[ "station", "train", "pull" ]
train pulling in to station .
3
[ "station", "train", "pull" ]
the train pulling into station
4
[ "hay", "eat", "horse" ]
A horse is eating hay.
4
[ "hay", "eat", "horse" ]
The horses are eating hay.
4
[ "hay", "eat", "horse" ]
A horse eats hay in the barn
5
[ "fan", "match", "watch" ]
watch a match with fans
5
[ "fan", "match", "watch" ]
the fans watch the match
5
[ "fan", "match", "watch" ]
a fan watches during the match
6
[ "mountain", "surround", "lake" ]
a lake surrounded by mountains .
6
[ "mountain", "surround", "lake" ]
lake from the surrounding mountains
6
[ "mountain", "surround", "lake" ]
one of the mountain ranges that surrounds lake .
7
[ "lay", "rug", "dog" ]
A dog laying on a rug.
7
[ "lay", "rug", "dog" ]
The dogs laid down on the rug
7
[ "lay", "rug", "dog" ]
Brown dog chews on bone while laying on the rug.
8
[ "painting", "wall", "hang" ]
hanging a painting on a wall at home
8
[ "painting", "wall", "hang" ]
paintings of horses hang on the walls .
8
[ "painting", "wall", "hang" ]
There is only one painting hanging on the wall.
9
[ "food", "carry", "tray" ]
boy carries a tray of food .
9
[ "food", "carry", "tray" ]
people carrying food on trays
9
[ "food", "carry", "tray" ]
The woman is carrying two trays of food.
10
[ "match", "stadium", "watch" ]
soccer fans watches a league match in a stadium
10
[ "match", "stadium", "watch" ]
A stadium full of people watching a tennis match.
10
[ "match", "stadium", "watch" ]
supporters watch the match from a hill outside the stadium
11
[ "paw", "lick", "cat" ]
A cat licks his paws.
11
[ "paw", "lick", "cat" ]
A cat is licking its paw
11
[ "paw", "lick", "cat" ]
the cat licks the pad of his front paw
12
[ "room", "tile", "wall" ]
a bath room with a toilet and tiled walls
12
[ "room", "tile", "wall" ]
Three men tile a wall in a large empty room
12
[ "room", "tile", "wall" ]
A wall mounted urinal in a checker tiled rest room.
13
[ "lake", "shore", "canoe" ]
canoe on a shore of lake .
13
[ "lake", "shore", "canoe" ]
canoe on shore with rainbow across the lake
13
[ "lake", "shore", "canoe" ]
Several canoes parked in the grass on the shore of a lake
14
[ "mountain", "skier", "way" ]
A skier on his way to the mountain.
14
[ "mountain", "skier", "way" ]
skiers make their way down the mountain
14
[ "mountain", "skier", "way" ]
A skier making her way down a snowy mountain.
15
[ "boat", "lake", "drive" ]
driving boat on a lake
15
[ "boat", "lake", "drive" ]
a boat is being driven through a lake
15
[ "boat", "lake", "drive" ]
A fisherman drives his boat on the lake
16
[ "grass", "horse", "eat" ]
A horse is eating grass.
16
[ "grass", "horse", "eat" ]
The horses are eating grass.
16
[ "grass", "horse", "eat" ]
The old horse ate grass all day.
17
[ "train", "come", "track" ]
train coming down the track
17
[ "train", "come", "track" ]
A train is coming along on a track.
17
[ "train", "come", "track" ]
a long train in coming down some tracks
18
[ "train", "track", "move" ]
train moving on the tracks
18
[ "train", "track", "move" ]
A red train is moving down a track
18
[ "train", "track", "move" ]
A train moves slowly on some empty tracks
19
[ "train", "station", "leave" ]
a train leaves the station
19
[ "train", "station", "leave" ]
a train leaving station bound
19
[ "train", "station", "leave" ]
a fast train about to leave station
20
[ "passenger", "train", "station" ]
train and passengers at the station
20
[ "passenger", "train", "station" ]
passengers leaving a train on a station
20
[ "passenger", "train", "station" ]
a train at station with no passengers joining
21
[ "train", "station", "arrive" ]
a train arrives at station
21
[ "train", "station", "arrive" ]
train arriving at the station
21
[ "train", "station", "arrive" ]
subway train arrives in the station
22
[ "station", "sit", "train" ]
a train sits at the station
22
[ "station", "sit", "train" ]
A train that is sitting in a station.
22
[ "station", "sit", "train" ]
A red train sitting at an empty station.
23
[ "wagon", "horse", "pull" ]
a tea of horses pull a wagon
23
[ "wagon", "horse", "pull" ]
horse pulling man on wagon .
23
[ "wagon", "horse", "pull" ]
A wagon is being pulled by horses.
24
[ "station", "stop", "train" ]
train is stopped at a station
24
[ "station", "stop", "train" ]
trains stopping at the station
24
[ "station", "stop", "train" ]
The empty train is stopped in the station.
25
[ "plane", "runway", "sit" ]
A plane sits on the runway
25
[ "plane", "runway", "sit" ]
An old plane is sitting on a runway.
25
[ "plane", "runway", "sit" ]
Two planes are sitting out on the runway.
26
[ "fly", "cloud", "plane" ]
plane flying into the clouds
26
[ "fly", "cloud", "plane" ]
flying plane against a cloud .
26
[ "fly", "cloud", "plane" ]
A plane flies over head in the clouds.
27
[ "herd", "dog", "sheep" ]
A dog herds a sheep.
27
[ "herd", "dog", "sheep" ]
A dog is herding sheep.
27
[ "herd", "dog", "sheep" ]
The dogs are herding sheep.
28
[ "boat", "sit", "beach" ]
boats sitting on the beach
28
[ "boat", "sit", "beach" ]
a boat is sitting up on a beach
28
[ "boat", "sit", "beach" ]
Pelicans sit on a blue boat at the beach.
29
[ "train", "come", "station" ]
a train coming into station
29
[ "train", "come", "station" ]
tube train comes to station .
29
[ "train", "come", "station" ]
train coming in to the station
30
[ "float", "sky", "cloud" ]
clouds floating in the sky
30
[ "float", "sky", "cloud" ]
clouds float through a blue sky
30
[ "float", "sky", "cloud" ]
shot of clouds that float across the sky
31
[ "eat", "grass", "elephant" ]
elephants pulling grass to eat .
31
[ "eat", "grass", "elephant" ]
An elephant is eating grass in Kenya.
31
[ "eat", "grass", "elephant" ]
a bunch of elephants are eating grass
32
[ "family", "time", "spend" ]
family spend time in the park
32
[ "family", "time", "spend" ]
spending time with the family
32
[ "family", "time", "spend" ]
family spend time at a holidays
33
[ "wall", "tile", "bathroom" ]
black walls and tiles in the bathroom
End of preview (truncated to 100 rows)

Dataset Card for "common_gen"

Dataset Summary

CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday scenario using these concepts.

CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

  • Size of downloaded dataset files: 1.76 MB
  • Size of the generated dataset: 6.88 MB
  • Total amount of disk used: 8.64 MB

An example of 'train' looks as follows.

{
    "concept_set_idx": 0,
    "concepts": ["ski", "mountain", "skier"],
    "target": "Three skiers are skiing on a snowy mountain."
}

Data Fields

The data fields are the same among all splits.

default

  • concept_set_idx: a int32 feature.
  • concepts: a list of string features.
  • target: a string feature.

Data Splits

name train validation test
default 67389 4018 1497

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

More Information Needed

Citation Information

@inproceedings{lin-etal-2020-commongen,
    title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
    author = "Lin, Bill Yuchen  and
      Zhou, Wangchunshu  and
      Shen, Ming  and
      Zhou, Pei  and
      Bhagavatula, Chandra  and
      Choi, Yejin  and
      Ren, Xiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165",
    doi = "10.18653/v1/2020.findings-emnlp.165",
    pages = "1823--1840"
}

Contributions

Thanks to @JetRunner, @yuchenlin, @thomwolf, @lhoestq for adding this dataset.

Models trained or fine-tuned on common_gen