Datasets:
concept_set_idx
int32 0
32.7k
| concepts
sequence | target
stringlengths 16
142
|
---|---|---|
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 |
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
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 1.85 MB
- Size of the generated dataset: 7.21 MB
- Total amount of disk used: 9.06 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
: aint32
feature.concepts
: alist
ofstring
features.target
: astring
feature.
Data Splits
name | train | validation | test |
---|---|---|---|
default | 67389 | 4018 | 1497 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
The dataset is licensed under MIT License.
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.
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