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id (string)sentence_A (string)sentence_B (string)label (class label)relatedness_score (float32)entailment_AB (string)entailment_BA (string)sentence_A_original (string)sentence_B_original (string)sentence_A_dataset (string)sentence_B_dataset (string)
"1"
"A group of kids is playing in a yard and an old man is standing in the background"
"A group of boys in a yard is playing and a man is standing in the background"
1 (neutral)
4.5
"A_neutral_B"
"B_neutral_A"
"A group of children playing in a yard, a man in the background."
"A group of children playing in a yard, a man in the background."
"FLICKR"
"FLICKR"
"2"
"A group of children is playing in the house and there is no man standing in the background"
"A group of kids is playing in a yard and an old man is standing in the background"
1 (neutral)
3.2
"A_contradicts_B"
"B_neutral_A"
"A group of children playing in a yard, a man in the background."
"A group of children playing in a yard, a man in the background."
"FLICKR"
"FLICKR"
"3"
"The young boys are playing outdoors and the man is smiling nearby"
"The kids are playing outdoors near a man with a smile"
0 (entailment)
4.7
"A_entails_B"
"B_entails_A"
"The children are playing outdoors, while a man smiles nearby."
"The children are playing outdoors, while a man smiles nearby."
"FLICKR"
"FLICKR"
"5"
"The kids are playing outdoors near a man with a smile"
"A group of kids is playing in a yard and an old man is standing in the background"
1 (neutral)
3.4
"A_neutral_B"
"B_neutral_A"
"A group of children playing in a yard, a man in the background."
"The children are playing outdoors, while a man smiles nearby."
"FLICKR"
"FLICKR"
"9"
"The young boys are playing outdoors and the man is smiling nearby"
"A group of kids is playing in a yard and an old man is standing in the background"
1 (neutral)
3.7
"A_neutral_B"
"B_neutral_A"
"A group of children playing in a yard, a man in the background."
"The children are playing outdoors, while a man smiles nearby."
"FLICKR"
"FLICKR"
"12"
"Two dogs are fighting"
"Two dogs are wrestling and hugging"
1 (neutral)
4
"A_neutral_B"
"B_neutral_A"
"Two dogs wrestle or hug."
"Two dogs wrestle or hug."
"FLICKR"
"FLICKR"
"14"
"A brown dog is attacking another animal in front of the man in pants"
"Two dogs are fighting"
1 (neutral)
3.5
"A_neutral_B"
"B_neutral_A"
"A brown dog is jumping up at another dog in front of the man in jeans."
"Two dogs wrestle or hug."
"FLICKR"
"FLICKR"
"18"
"A brown dog is attacking another animal in front of the man in pants"
"Two dogs are wrestling and hugging"
1 (neutral)
3.2
"A_neutral_B"
"B_neutral_A"
"A brown dog is jumping up at another dog in front of the man in jeans."
"Two dogs wrestle or hug."
"FLICKR"
"FLICKR"
"25"
"Nobody is riding the bicycle on one wheel"
"A person in a black jacket is doing tricks on a motorbike"
1 (neutral)
2.8
"A_contradicts_B"
"B_neutral_A"
"A person rides their bicycle onto a rock and balances on one wheel."
"A person in a black jacket doing tricks on a motorbike."
"FLICKR"
"FLICKR"
"26"
"A person is riding the bicycle on one wheel"
"A man in a black jacket is doing tricks on a motorbike"
1 (neutral)
3.7
"A_neutral_B"
"B_neutral_A"
"A person in a black jacket doing tricks on a motorbike."
"A person rides their bicycle onto a rock and balances on one wheel."
"FLICKR"
"FLICKR"
"28"
"A person on a black motorbike is doing tricks with a jacket"
"A person is riding the bicycle on one wheel"
1 (neutral)
3.4
"A_neutral_B"
"B_neutral_A"
"A person rides their bicycle onto a rock and balances on one wheel."
"A person in a black jacket doing tricks on a motorbike."
"FLICKR"
"FLICKR"
"30"
"A man with a jersey is dunking the ball at a basketball game"
"The ball is being dunked by a man with a jersey at a basketball game"
0 (entailment)
4.9
"A_entails_B"
"B_entails_A"
"A man with jersey 23 is dunking the ball at a basket ball game."
"A man with jersey 23 is dunking the ball at a basket ball game."
"FLICKR"
"FLICKR"
"35"
"A man with a jersey is dunking the ball at a basketball game"
"A man who is playing dunks the basketball into the net and a crowd is in background"
1 (neutral)
3.6
"A_neutral_B"
"B_neutral_A"
"A man with jersey 23 is dunking the ball at a basket ball game."
"Player number 23 dunks the basketball into the net with a crowd in the background."
"FLICKR"
"FLICKR"
"40"
"The player is dunking the basketball into the net and a crowd is in background"
"A man with a jersey is dunking the ball at a basketball game"
1 (neutral)
3.8
"A_neutral_B"
"B_neutral_A"
"A man with jersey 23 is dunking the ball at a basket ball game."
"Player number 23 dunks the basketball into the net with a crowd in the background."
"FLICKR"
"FLICKR"
"42"
"Two people are kickboxing and spectators are not watching"
"Two people are kickboxing and spectators are watching"
2 (contradiction)
3.4
"A_contradicts_B"
"B_contradicts_A"
"Two people kick boxing, with others spectating."
"Two people kick boxing, with others spectating."
"FLICKR"
"FLICKR"
"44"
"Two young women are sparring in a kickboxing fight"
"Two women are sparring in a kickboxing match"
0 (entailment)
4.9
"A_entails_B"
"B_entails_A"
"Two woman spar in a kickboxing match."
"Two woman spar in a kickboxing match."
"FLICKR"
"FLICKR"
"45"
"Two young women are not sparring in a kickboxing fight"
"Two women are sparring in a kickboxing match"
1 (neutral)
3.9
"A_contradicts_B"
"B_neutral_A"
"Two woman spar in a kickboxing match."
"Two woman spar in a kickboxing match."
"FLICKR"
"FLICKR"
"47"
"Two people are kickboxing and spectators are watching"
"Two young women are not sparring in a kickboxing fight"
1 (neutral)
3.415
"A_neutral_B"
"B_neutral_A"
"Two people kick boxing, with others spectating."
"Two woman spar in a kickboxing match."
"FLICKR"
"FLICKR"
"49"
"Two women are sparring in a kickboxing match"
"Two people are kickboxing and spectators are not watching"
1 (neutral)
3.7
"A_neutral_B"
"B_neutral_A"
"Two woman spar in a kickboxing match."
"Two people kick boxing, with others spectating."
"FLICKR"
"FLICKR"
"55"
"Three boys are jumping in the leaves"
"Three kids are jumping in the leaves"
0 (entailment)
4.4
"A_entails_B"
"B_neutral_A"
"Three kids jumping in leaves."
"Three kids jumping in leaves."
"FLICKR"
"FLICKR"
"56"
"Three kids are sitting in the leaves"
"Three kids are jumping in the leaves"
1 (neutral)
3.8
"A_contradicts_B"
"B_neutral_A"
"Three kids jumping in leaves."
"Three kids jumping in leaves."
"FLICKR"
"FLICKR"
"58"
"Children in red shirts are playing in the leaves"
"Three kids are sitting in the leaves"
1 (neutral)
3.5
"A_neutral_B"
"B_neutral_A"
"Children in red shirts play in the leaves."
"Three kids jumping in leaves."
"FLICKR"
"FLICKR"
"62"
"Children in red shirts are playing in the leaves"
"Three kids are jumping in the leaves"
1 (neutral)
4
"A_neutral_B"
"B_neutral_A"
"Children in red shirts play in the leaves."
"Three kids jumping in leaves."
"FLICKR"
"FLICKR"
"65"
"Two angels are making snow on the lying children"
"Two children are lying in the snow and are making snow angels"
1 (neutral)
2.9
"A_neutral_B"
"B_neutral_A"
"Two children lying in the snow making snow angels"
"Two children lying in the snow making snow angels"
"FLICKR"
"FLICKR"
"70"
"Two children are lying in the snow and are drawing angels"
"Two people in snowsuits are lying in the snow and making snow angels"
1 (neutral)
4.1
"A_neutral_B"
"B_neutral_A"
"Two people in snowsuits laying in the snow making snow angels"
"Two children lying in the snow making snow angels"
"FLICKR"
"FLICKR"
"72"
"Two people in snowsuits are lying in the snow and making snow angels"
"Two angels are making snow on the lying children"
1 (neutral)
2.5
"A_neutral_B"
"B_neutral_A"
"Two people in snowsuits laying in the snow making snow angels"
"Two children lying in the snow making snow angels"
"FLICKR"
"FLICKR"
"73"
"Two children are lying in the snow and are making snow angels"
"Two people in snowsuits are lying in the snow and making snow angels"
1 (neutral)
4.2
"A_neutral_B"
"B_neutral_A"
"Two children lying in the snow making snow angels"
"Two people in snowsuits laying in the snow making snow angels"
"FLICKR"
"FLICKR"
"77"
"People wearing costumes are gathering in a forest and are looking in the same direction"
"Masked people are looking in the same direction in a forest"
0 (entailment)
4.4
"A_entails_B"
"B_neutral_A"
"People with costumes are gathered in a wooded area looking the same direction."
"People with costumes are gathered in a wooded area looking the same direction."
"FLICKR"
"FLICKR"
"78"
"People wearing costumes are gathering in a forest and are looking in the same direction"
"People wearing costumes are scattering in a forest and are looking in different directions"
1 (neutral)
3.2
"A_neutral_B"
"B_contradicts_A"
"People with costumes are gathered in a wooded area looking the same direction."
"People with costumes are gathered in a wooded area looking the same direction."
"FLICKR"
"FLICKR"
"79"
"People are looking at some costumes gathered in the vicinity of the forest"
"People wearing costumes are gathering in a forest and are looking in the same direction"
1 (neutral)
3.635
"A_neutral_B"
"B_neutral_A"
"People with costumes are gathered in a wooded area looking the same direction."
"People with costumes are gathered in a wooded area looking the same direction."
"FLICKR"
"FLICKR"
"81"
"A little girl is looking at a woman in costume"
"People wearing costumes are scattering in a forest and are looking in different directions"
1 (neutral)
2.4
"A_neutral_B"
"B_neutral_A"
"The back of a woman in costume ; little girl looks at her"
"People with costumes are gathered in a wooded area looking the same direction."
"FLICKR"
"FLICKR"
"82"
"A little girl is looking at a woman in costume"
"People are looking at some costumes gathered in the vicinity of the forest"
1 (neutral)
2.6
"A_neutral_B"
"B_neutral_A"
"The back of a woman in costume ; little girl looks at her"
"People with costumes are gathered in a wooded area looking the same direction."
"FLICKR"
"FLICKR"
"83"
"A young girl is looking at a woman in costume"
"People wearing costumes are gathering in a forest and are looking in the same direction"
1 (neutral)
2.2
"A_neutral_B"
"B_neutral_A"
"People with costumes are gathered in a wooded area looking the same direction."
"The back of a woman in costume ; little girl looks at her"
"FLICKR"
"FLICKR"
"84"
"People wearing costumes are gathering in a forest and are looking in the same direction"
"The little girl is looking at a man in costume"
1 (neutral)
3
"A_neutral_B"
"B_neutral_A"
"People with costumes are gathered in a wooded area looking the same direction."
"The back of a woman in costume ; little girl looks at her"
"FLICKR"
"FLICKR"
"85"
"People wearing costumes are gathering in a forest and are looking in the same direction"
"A little girl in costume looks like a woman"
1 (neutral)
2
"A_neutral_B"
"B_neutral_A"
"People with costumes are gathered in a wooded area looking the same direction."
"The back of a woman in costume ; little girl looks at her"
"FLICKR"
"FLICKR"
"87"
"A lone biker is jumping in the air"
"A biker is jumping in the air, alone"
0 (entailment)
5
"A_entails_B"
"B_entails_A"
"a lone bicyclists jumping in the air over a ramp."
"a lone bicyclists jumping in the air over a ramp."
"FLICKR"
"FLICKR"
"88"
"There is no biker jumping in the air"
"A lone biker is jumping in the air"
2 (contradiction)
4.2
"A_contradicts_B"
"B_contradicts_A"
"a lone bicyclists jumping in the air over a ramp."
"a lone bicyclists jumping in the air over a ramp."
"FLICKR"
"FLICKR"
"90"
"A man is jumping into an empty pool"
"A man is jumping into a full pool"
2 (contradiction)
3
"A_contradicts_B"
"B_contradicts_A"
"A man does bike jumps in the dark in an empty pool."
"A man does bike jumps in the dark in an empty pool."
"FLICKR"
"FLICKR"
"91"
"A man is jumping into an empty pool"
"The man's jumper is in the empty pool"
1 (neutral)
3.1
"A_neutral_B"
"B_neutral_A"
"A man does bike jumps in the dark in an empty pool."
"A man does bike jumps in the dark in an empty pool."
"FLICKR"
"FLICKR"
"93"
"A lone biker is jumping in the air"
"A man is jumping into a full pool"
1 (neutral)
1.7
"A_neutral_B"
"B_neutral_A"
"A man does bike jumps in the dark in an empty pool."
"a lone bicyclists jumping in the air over a ramp."
"FLICKR"
"FLICKR"
"94"
"The man's jumper is in the empty pool"
"A lone biker is jumping in the air"
1 (neutral)
1.4
"A_neutral_B"
"B_neutral_A"
"a lone bicyclists jumping in the air over a ramp."
"A man does bike jumps in the dark in an empty pool."
"FLICKR"
"FLICKR"
"97"
"A lone biker is jumping in the air"
"A man is jumping into an empty pool"
1 (neutral)
1.5
"A_neutral_B"
"B_neutral_A"
"a lone bicyclists jumping in the air over a ramp."
"A man does bike jumps in the dark in an empty pool."
"FLICKR"
"FLICKR"
"98"
"Four kids are doing backbends in the park"
"Four children are doing backbends in the park"
0 (entailment)
4.8
"A_entails_B"
"B_entails_A"
"Four children do backbends in the park."
"Four children do backbends in the park."
"FLICKR"
"FLICKR"
"99"
"Four children are doing backbends in the gym"
"Four children are doing backbends in the park"
1 (neutral)
3.8
"A_neutral_B"
"B_neutral_A"
"Four children do backbends in the park."
"Four children do backbends in the park."
"FLICKR"
"FLICKR"
"100"
"Four girls are doing backbends and playing in the garden"
"Four girls are doing backbends and playing outdoors"
0 (entailment)
4.1
"A_entails_B"
"B_neutral_A"
"Four girls do backbends while playing outdoors."
"Four girls do backbends while playing outdoors."
"FLICKR"
"FLICKR"
"107"
"A man who is playing is running with the ball in his hands"
"A player is running with the ball"
0 (entailment)
4.3
"A_entails_B"
"B_neutral_A"
"A running back running with the football."
"A running back running with the football."
"FLICKR"
"FLICKR"
"112"
"Two groups of people are playing football"
"A player is running with the ball"
1 (neutral)
2.1
"A_neutral_B"
"B_neutral_A"
"A running back running with the football."
"Two teams compete in football."
"FLICKR"
"FLICKR"
"113"
"Two teams are competing in a baseball game"
"A player is running with the ball"
1 (neutral)
3
"A_neutral_B"
"B_neutral_A"
"A running back running with the football."
"Two teams compete in football."
"FLICKR"
"FLICKR"
"117"
"A player is running with the ball"
"Two teams are competing in a football match"
1 (neutral)
2.6
"A_neutral_B"
"B_neutral_A"
"A running back running with the football."
"Two teams compete in football."
"FLICKR"
"FLICKR"
"120"
"Five wooden stands are in front of each child's hut"
"Five children are standing in front of a wooden hut"
1 (neutral)
3.2
"A_neutral_B"
"B_neutral_A"
"Five children are standing in front of a wooden hut."
"Five children are standing in front of a wooden hut."
"FLICKR"
"FLICKR"
"122"
"Five kids are standing close together and one kid has a gun"
"Five kids are standing close together and none of the kids has a gun"
2 (contradiction)
3.7
"A_contradicts_B"
"B_contradicts_A"
"There are five kids standing next to each other, and the one in the red has a gun."
"There are five kids standing next to each other, and the one in the red has a gun."
"FLICKR"
"FLICKR"
"124"
"Five kids are standing close together and none of the kids has a gun"
"Five children are standing in front of a wooden hut"
1 (neutral)
2.6
"A_neutral_B"
"B_neutral_A"
"Five children are standing in front of a wooden hut."
"There are five kids standing next to each other, and the one in the red has a gun."
"FLICKR"
"FLICKR"
"126"
"Five children are standing in a wooden hut"
"Five kids are standing close together and one kid has a gun"
1 (neutral)
2.7
"A_neutral_B"
"B_neutral_A"
"There are five kids standing next to each other, and the one in the red has a gun."
"Five children are standing in front of a wooden hut."
"FLICKR"
"FLICKR"
"127"
"Five wooden stands are in front of each child's hut"
"Five kids are standing close together and one kid has a gun"
1 (neutral)
2.3
"A_neutral_B"
"B_neutral_A"
"There are five kids standing next to each other, and the one in the red has a gun."
"Five children are standing in front of a wooden hut."
"FLICKR"
"FLICKR"
"129"
"An old man is sitting in a field"
"A man is sitting in a field"
0 (entailment)
4.4
"A_entails_B"
"B_neutral_A"
"A man sitting cross-legged in a field"
"A man sitting cross-legged in a field"
"FLICKR"
"FLICKR"
"130"
"A man is sitting in a field"
"A man is running in a field"
1 (neutral)
2.6
"A_neutral_B"
"B_neutral_A"
"A man sitting cross-legged in a field"
"A man sitting cross-legged in a field"
"FLICKR"
"FLICKR"
"131"
"A person is wearing a hat and is sitting on the grass"
"A person is sitting in a field and is wearing a hat"
1 (neutral)
4.1
"A_neutral_B"
"B_neutral_A"
"A person wearing a hat sits on the grass with a mountain behind him."
"A person wearing a hat sits on the grass with a mountain behind him."
"FLICKR"
"FLICKR"
"133"
"A person is sitting and wearing a grass hat"
"A person is wearing a hat and is sitting on the grass"
1 (neutral)
3.4
"A_neutral_B"
"B_neutral_A"
"A person wearing a hat sits on the grass with a mountain behind him."
"A person wearing a hat sits on the grass with a mountain behind him."
"FLICKR"
"FLICKR"
"136"
"A person is sitting and wearing a grass hat"
"A man is sitting in a field"
1 (neutral)
3.3
"A_neutral_B"
"B_neutral_A"
"A man sitting cross-legged in a field"
"A person wearing a hat sits on the grass with a mountain behind him."
"FLICKR"
"FLICKR"
"139"
"A man is sitting in a field"
"A person is wearing a hat and is sitting on the grass"
1 (neutral)
3.8
"A_neutral_B"
"B_neutral_A"
"A man sitting cross-legged in a field"
"A person wearing a hat sits on the grass with a mountain behind him."
"FLICKR"
"FLICKR"
"140"
"The current is being ridden by a group of friends in a raft"
"A group of friends are riding the current in a raft"
0 (entailment)
4.9
"A_entails_B"
"B_entails_A"
"A group of friends ride the current in a raft."
"A group of friends ride the current in a raft."
"FLICKR"
"FLICKR"
"141"
"A group of friends are riding the current in a raft"
"A group is not riding the current in a raft"
2 (contradiction)
3.7
"A_contradicts_B"
"B_contradicts_A"
"A group of friends ride the current in a raft."
"A group of friends ride the current in a raft."
"FLICKR"
"FLICKR"
"146"
"The current is being ridden by a group of friends in a raft"
"This group of people is practicing water safety and wearing preservers"
1 (neutral)
3.2
"A_neutral_B"
"B_neutral_A"
"this group of people are practicing water safety by wearing preservers while in a raft on the river"
"A group of friends ride the current in a raft."
"FLICKR"
"FLICKR"
"148"
"A group of friends are riding the current in a raft"
"This group of people is practicing water safety and wearing preservers"
1 (neutral)
3.1
"A_neutral_B"
"B_neutral_A"
"A group of friends ride the current in a raft."
"this group of people are practicing water safety by wearing preservers while in a raft on the river"
"FLICKR"
"FLICKR"
"150"
"A deer is jumping over a fence"
"A deer isn't jumping over the fence"
2 (contradiction)
3.9
"A_contradicts_B"
"B_contradicts_A"
"A deer is jumping over a fence."
"A deer is jumping over a fence."
"FLICKR"
"FLICKR"
"159"
"People are walking inside a building that has many murals on it"
"People are walking outside a building that has many murals on it"
1 (neutral)
3.4
"A_neutral_B"
"B_neutral_A"
"People walk outside of a building that has many murals painted on it."
"People walk outside of a building that has many murals painted on it."
"FLICKR"
"FLICKR"
"161"
"Several people are in front of a colorful building"
"Nobody is in front of the colorful building"
2 (contradiction)
3.5
"A_contradicts_B"
"B_contradicts_A"
"Several people are in a front of a colorful painted building."
"Several people are in a front of a colorful painted building."
"FLICKR"
"FLICKR"
"163"
"People are walking outside a building that has many murals on it"
"Nobody is in front of the colorful building"
1 (neutral)
3.6
"A_contradicts_B"
"B_neutral_A"
"People walk outside of a building that has many murals painted on it."
"Several people are in a front of a colorful painted building."
"FLICKR"
"FLICKR"
"164"
"People are walking outside the building that has several murals on it"
"Several people are in front of a colorful building"
0 (entailment)
3.6
"A_entails_B"
"B_neutral_A"
"Several people are in a front of a colorful painted building."
"People walk outside of a building that has many murals painted on it."
"FLICKR"
"FLICKR"
"170"
"A family is watching a little boy who is hitting a baseball"
"A family is watching a little boy who is missing a baseball"
1 (neutral)
3.9
"A_contradicts_B"
"B_neutral_A"
"A little boy hits a baseball while his family watches"
"A little boy hits a baseball while his family watches"
"FLICKR"
"FLICKR"
"173"
"A child is hitting a baseball"
"A family is watching a little boy who is missing a baseball"
1 (neutral)
3.015
"A_neutral_B"
"B_neutral_A"
"A child hits a baseball."
"A little boy hits a baseball while his family watches"
"FLICKR"
"FLICKR"
"176"
"A child is missing a baseball"
"A family is watching a little boy who is hitting a baseball"
1 (neutral)
2.7
"A_neutral_B"
"B_neutral_A"
"A little boy hits a baseball while his family watches"
"A child hits a baseball."
"FLICKR"
"FLICKR"
"183"
"A purple crowd of people is eating on various red lit restaurant tables"
"Various people are eating at red tables in a crowded restaurant with purple lights"
1 (neutral)
3.3
"A_neutral_B"
"B_neutral_A"
"Various people are eating at red tables in a crowded restaurant with purple lights."
"Various people are eating at red tables in a crowded restaurant with purple lights."
"FLICKR"
"FLICKR"
"184"
"A large group of Asian people is eating at a restaurant"
"Various customers are eating in a crowded restaurant with purple lights"
1 (neutral)
2.9
"A_neutral_B"
"B_neutral_A"
"A large group of asian people are eating at a restaurant."
"Various people are eating at red tables in a crowded restaurant with purple lights."
"FLICKR"
"FLICKR"
"186"
"A purple crowd of people is eating on various red lit restaurant tables"
"A large group of Asian people is eating at a restaurant"
1 (neutral)
3.1
"A_neutral_B"
"B_neutral_A"
"Various people are eating at red tables in a crowded restaurant with purple lights."
"A large group of asian people are eating at a restaurant."
"FLICKR"
"FLICKR"
"188"
"Various people are eating at red tables in a crowded restaurant with purple lights"
"A small group of people is waiting to eat in a restaurant"
1 (neutral)
3.2
"A_neutral_B"
"B_neutral_A"
"Various people are eating at red tables in a crowded restaurant with purple lights."
"A large group of asian people are eating at a restaurant."
"FLICKR"
"FLICKR"
"192"
"A motorcycle rider is standing up on the seat of a white motorcycle"
"No motorcycle rider is standing up on the seat of a motorcycle"
2 (contradiction)
3.8
"A_contradicts_B"
"B_contradicts_A"
"A motorcycle rider starts to sand up on the seat of his white motorcycle."
"A motorcycle rider starts to sand up on the seat of his white motorcycle."
"FLICKR"
"FLICKR"
"194"
"Nobody is on a motorcycle and is standing on the seat"
"Someone is on a black and white motorcycle and is standing on the seat"
2 (contradiction)
3.7
"A_contradicts_B"
"B_contradicts_A"
"Someone is on a black and white motorcycle and putting their feet on the seat."
"Someone is on a black and white motorcycle and putting their feet on the seat."
"FLICKR"
"FLICKR"
"200"
"A motorcyclist is riding a motorbike dangerously along a roadway"
"A motorcyclist is riding a motorbike along a roadway"
0 (entailment)
4.6
"A_entails_B"
"B_neutral_A"
"A motorcyclist is riding their sponsored car along a roadway that has recently turned."
"A motorcyclist is riding their sponsored car along a roadway that has recently turned."
"FLICKR"
"FLICKR"
"201"
"There is no motorcyclist riding a motorbike along a roadway"
"A motorcyclist is riding a motorbike along a roadway"
2 (contradiction)
3.7
"A_contradicts_B"
"B_contradicts_A"
"A motorcyclist is riding their sponsored car along a roadway that has recently turned."
"A motorcyclist is riding their sponsored car along a roadway that has recently turned."
"FLICKR"
"FLICKR"
"202"
"A man with a helmet painted red is riding a blue motorcycle down the road"
"A motorcyclist with a red helmet is riding a blue motorcycle down the road"
0 (entailment)
4.8
"A_entails_B"
"B_entails_A"
"A motorcyclist with a red helmet rides his blue motorcycle down the road."
"A motorcyclist with a red helmet rides his blue motorcycle down the road."
"FLICKR"
"FLICKR"
"205"
"A motorcyclist without a helmet is waiting on a blue motorcycle near the road"
"A motorcyclist is riding a motorbike along a roadway"
1 (neutral)
3.3
"A_neutral_B"
"B_neutral_A"
"A motorcyclist is riding their sponsored car along a roadway that has recently turned."
"A motorcyclist with a red helmet rides his blue motorcycle down the road."
"FLICKR"
"FLICKR"
"215"
"Two dogs are playing by a tree"
"A dog is catching a stick in the air and another is watching"
1 (neutral)
3.7
"A_neutral_B"
"B_neutral_A"
"Two dogs play by a tree."
"A dog leaps high in the air while another watches."
"FLICKR"
"FLICKR"
"216"
"Two dogs are playing by a tree"
"There is no dog leaping in the air"
1 (neutral)
2.7
"A_neutral_B"
"B_neutral_A"
"Two dogs play by a tree."
"A dog leaps high in the air while another watches."
"FLICKR"
"FLICKR"
"217"
"Two dogs are playing by a tree"
"A dog is leaping high in the air and another is watching"
1 (neutral)
3
"A_neutral_B"
"B_neutral_A"
"A dog leaps high in the air while another watches."
"Two dogs play by a tree."
"FLICKR"
"FLICKR"
"223"
"A girl in white is dancing"
"The dancer is dancing in front of the sound equipment"
1 (neutral)
4
"A_neutral_B"
"B_neutral_A"
"A girl in white dances."
"The blonde girl is dancing on stage in front of the sound equipment."
"FLICKR"
"FLICKR"
"224"
"A girl in white is dancing"
"The blond girl is dancing behind the sound equipment"
1 (neutral)
3.3
"A_neutral_B"
"B_neutral_A"
"A girl in white dances."
"The blonde girl is dancing on stage in front of the sound equipment."
"FLICKR"
"FLICKR"
"226"
"A girl is wearing white clothes and is dancing"
"The blond girl is dancing in front of the sound equipment"
1 (neutral)
3.5
"A_neutral_B"
"B_neutral_A"
"A girl in white dances."
"The blonde girl is dancing on stage in front of the sound equipment."
"FLICKR"
"FLICKR"
"227"
"The blond girl is dancing in front of the sound equipment"
"There is no girl in white dancing"
1 (neutral)
3.3
"A_neutral_B"
"B_neutral_A"
"The blonde girl is dancing on stage in front of the sound equipment."
"A girl in white dances."
"FLICKR"
"FLICKR"
"232"
"Three Asian kids are dancing and a man is looking"
"Three Asian kids are dancing and there is no man looking"
2 (contradiction)
3.9
"A_contradicts_B"
"B_contradicts_A"
"Three Asian kids dance while a man looks on."
"Three Asian kids dance while a man looks on."
"FLICKR"
"FLICKR"
"233"
"Three Asian kids are dancing and a man is looking"
"An Asian man is dancing and three kids are looking"
1 (neutral)
3.7
"A_neutral_B"
"B_neutral_A"
"Three Asian kids dance while a man looks on."
"Three Asian kids dance while a man looks on."
"FLICKR"
"FLICKR"
"234"
"The children of a family are playing and waiting"
"Three Asian kids are dancing and a serious man is looking"
1 (neutral)
1.9
"A_neutral_B"
"B_neutral_A"
"The children of a family play while they wait."
"Three Asian kids dance while a man looks on."
"FLICKR"
"FLICKR"
"237"
"The children of a family are patiently playing and waiting"
"Three Asian kids are dancing and a man is looking"
1 (neutral)
2.3
"A_neutral_B"
"B_neutral_A"
"Three Asian kids dance while a man looks on."
"The children of a family play while they wait."
"FLICKR"
"FLICKR"
"238"
"There are no children playing and waiting"
"Three Asian kids are dancing and a man is looking"
1 (neutral)
1.6
"A_neutral_B"
"B_neutral_A"
"Three Asian kids dance while a man looks on."
"The children of a family play while they wait."
"FLICKR"
"FLICKR"
"240"
"A woman is wearing an Egyptian hat on her head"
"A woman is wearing an Egyptian headdress"
0 (entailment)
4.3
"A_entails_B"
"B_entails_A"
"A woman wears an Egytian-like headdress."
"A woman wears an Egytian-like headdress."
"FLICKR"
"FLICKR"
"241"
"A woman is wearing an Egyptian headdress"
"A woman is wearing an Indian headdress"
1 (neutral)
4
"A_neutral_B"
"B_neutral_A"
"A woman wears an Egytian-like headdress."
"A woman wears an Egytian-like headdress."
"FLICKR"
"FLICKR"
"248"
"The black woman is wearing glasses over the headdress"
"A woman is wearing an Egyptian headdress"
1 (neutral)
3.6
"A_neutral_B"
"B_neutral_A"
"The woman in glasses is wearing a black head dress."
"A woman wears an Egytian-like headdress."
"FLICKR"
"FLICKR"
"249"
"A woman is wearing an Egyptian hat on her head"
"The woman is wearing glasses and a black headdress"
1 (neutral)
2.5
"A_neutral_B"
"B_neutral_A"
"The woman in glasses is wearing a black head dress."
"A woman wears an Egytian-like headdress."
"FLICKR"
"FLICKR"
"254"
"A hiker is on top of the mountain and is dancing"
"There is no hiker dancing on top of the mountain"
2 (contradiction)
3.2
"A_contradicts_B"
"B_contradicts_A"
"This hiker does a dance of joy on top of the mountain."
"This hiker does a dance of joy on top of the mountain."
"FLICKR"
"FLICKR"
"256"
"There is no man on a rock high above some trees standing in a strange position"
"A man is on a rock high above some trees and is standing in a strange position"
2 (contradiction)
4.3
"A_contradicts_B"
"B_contradicts_A"
"A man stands in a strange position on a rock high above some trees."
"A man stands in a strange position on a rock high above some trees."
"FLICKR"
"FLICKR"
End of preview (truncated to 100 rows)

Dataset Card for sick

Dataset Summary

Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.

Supported Tasks and Leaderboards

[Needs More Information]

Languages

The dataset is in English.

Dataset Structure

Data Instances

Example instance:

{
    "entailment_AB": "A_neutral_B",
    "entailment_BA": "B_neutral_A",
    "label": 1,
    "id": "1",
    "relatedness_score": 4.5,
    "sentence_A": "A group of kids is playing in a yard and an old man is standing in the background",
    "sentence_A_dataset": "FLICKR",
    "sentence_A_original": "A group of children playing in a yard, a man in the background.",
    "sentence_B": "A group of boys in a yard is playing and a man is standing in the background",
    "sentence_B_dataset": "FLICKR",
    "sentence_B_original": "A group of children playing in a yard, a man in the background."
}

Data Fields

  • pair_ID: sentence pair ID
  • sentence_A: sentence A
  • sentence_B: sentence B
  • label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
  • relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
  • entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
  • entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
  • sentence_A_original: original sentence from which sentence A is derived
  • sentence_B_original: original sentence from which sentence B is derived
  • sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
  • sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)

Data Splits

Train Trial Test 4439 495 4906

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

@inproceedings{marelli-etal-2014-sick,
    title = "A {SICK} cure for the evaluation of compositional distributional semantic models",
    author = "Marelli, Marco  and
      Menini, Stefano  and
      Baroni, Marco  and
      Bentivogli, Luisa  and
      Bernardi, Raffaella  and
      Zamparelli, Roberto",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf",
    pages = "216--223",
}

Contributions

Thanks to @calpt for adding this dataset.

Models trained or fine-tuned on sick