Explanation: Commonsense Reasoning

Explanation: Textual Entailment -> Deductive Reasoning

In this task, you're given a statement and three sentences as choices. Your job is to determine which sentence can be inferred from the statement. Incorrect choices change the meaning in important ways or have details that are not mentioned in the statement. Indicate your answer as 1,2, or 3 corresponding to the choice number of the selected sentence.

Input: Statement: Next to the MGM Grand you will find M and M World, four stories of merchandise and memorabilia dedicated to the candy that doesn't melt in your hand. Choices: 1. The candy has many fans who love its attractions. 2. There's four stories of memorabilia dedicated to a candy. 3. That particular candy melts and becomes difficult to eat.

Output: 2

Explanation: The candy may or may not be popular, and does not melt and become difficult to eat. Hence the second choice is the only one that fully agrees with the statement.

Input: Statment: I've forgotten his name now, confessed Tuppence. Choices: 1. Tuppence forgot his name. 2.Tuppence remembered his name later. 3. Tuppence never could forget his name.

Output: 1

Explanation: Tuppence may or may not have later remembered the name, and has definitely forgotten it. Hence the first choice is the only one that fully agrees with the statement.

Input: Statement: One of the first organizational realignments taking place is in the Office of the Taxpayer Advocate. Choices: 1. The office of the taxpayer advocate is the last to be realigned. 2. The realignment is taking place over a few weeks. 3. The office of the taxpayer advocate is having an organizational realignment.

Output: 3

Explanation: The office is one of the first to be reorganized, and the duration of this is not know. Hence, the third choice is the only one that fully agrees with the statement.

Input: Statement: yeah i tell you what though if you go price some of those tennis shoes i can see why now you know they're getting up in the hundred dollar range. Choices: 1. The tennis shoes have a range of prices. 2. The tennis shoes can be in the hundred dollar range. 3. The tennis shoes are not over hundred dollars.

Output: 2

Explanation: The shoes' price range is not known, and they definitely cost more than a hundred dollars. Hence, the second choice is the only one that fully agrees with the statement.

Input: Statement: In 1994, fraction of households owning a personal computer is 0.25. Choices: 1. In 1994, 0.25 of households owned a PC, which was a high sum at the time. 2. In 1984, 0.8 of households owned a PC. In 1994, 0.25 of households owned a PC.

Output: 1

Explanation: It is not known whether 0.25 is considered a high fraction of the households, so the first choice is incorrect.

Input: Statement: You could say you were in an auto accident, and the ambulance driver took you straight to Dr. Famous' office. Choices: 1. Ambulance drivers knew Dr Famous was great for treating famous people. 2. Ambulance drivers just took you to the hospital. 3. Ambulance drivers knew Dr Famous.

Output: 2

Explanation: The ambulance took the person to Dr. Famous, and not a hospital, so the second choice is incorrect.

Input: Statement: The union has about 4000 members in Canada. Choices: 1. There's around 4000 Canadians in the union. 2. Toronto has Canada's biggest concentration of union members. 3. There are 100 members in the union that live in Canada.

Output: 3

Explanation: Based on the statement the union has about 4000 members in canada, so the third choice is incorrect.

Input: Statement: First to arrive was the Tang clan, which established a number of walled villages in the New Territories that still exist today. Choices: 1. The clan was not known for building. 2. Before the arrival of the Tang clan, the area was not inhabited by anyone. 3. Thanks to the Tang clan, there are a lot of walled villages that exist today.

Output: 1

Explanation: This statement doesn't mention whether clan is known for building or not. Hence, the first choice is incorrect.

Input: Statement: isn't it awful Choices: 1. This isn't too bad. 2. It's not as bad as past situations. 3. That is horrific.

Output: 2

Explanation: In this example, the statement implies that it's awful. Also, we don't know about the past situations. So, the second choice is incorrect.

Input: Statement: Construction of the offices obliterated much of the archaeological dig which had unearthed the original layout of the ninth-century quay, but the Viking artifacts that were found are on view in the National Museum  and in the Viking Adventure exhibit. Choices:  1. The Viking Adventure exhibit displays Thor's hammer. 2. The National museum has Viking artifacts on display. 3. Construction of the offices was permanently halted when Viking artifacts were found.

Output: The Viking Adventure exhibit displays Thor's hammer.

Explanation: You should only answer with the choice number. Here the acceptable answer is "1".

History
Fiction
Dialogue
Law
Government and Politics

Input: Statement: Formal and informal areas are landscaped with pools and fountains, while terraces tumble down the hillsides. Choices:  1. Pools and fountains were used in the landscaping of formal areas. 2. There were no pools or fountains in the informal areas. 3. The architect of the pools and fountains drew inspiration from the gardens of Versailles.

Output: 1


Explanation: Commonsense Reasoning

Input: Statement: Next to the MGM Grand you will find M and M World, four stories of merchandise and memorabilia dedicated to the candy that doesn't melt in your hand. Choices: 1. The candy has many fans who love its attractions. 2. There's four stories of memorabilia dedicated to a candy. 3. That particular candy melts and becomes difficult to eat.

Output: 2

Input: Statment: I've forgotten his name now, confessed Tuppence. Choices: 1. Tuppence forgot his name. 2.Tuppence remembered his name later. 3. Tuppence never could forget his name.

Output: 1

Input: Statement: One of the first organizational realignments taking place is in the Office of the Taxpayer Advocate. Choices: 1. The office of the taxpayer advocate is the last to be realigned. 2. The realignment is taking place over a few weeks. 3. The office of the taxpayer advocate is having an organizational realignment.

Output: 3

Input: Statement: yeah i tell you what though if you go price some of those tennis shoes i can see why now you know they're getting up in the hundred dollar range. Choices: 1. The tennis shoes have a range of prices. 2. The tennis shoes can be in the hundred dollar range. 3. The tennis shoes are not over hundred dollars.

Output: 2

Input: Statement: In 1994, fraction of households owning a personal computer is 0.25. Choices: 1. In 1994, 0.25 of households owned a PC, which was a high sum at the time. 2. In 1984, 0.8 of households owned a PC. In 1994, 0.25 of households owned a PC.

Output: 1

Input: Statement: You could say you were in an auto accident, and the ambulance driver took you straight to Dr. Famous' office. Choices: 1. Ambulance drivers knew Dr Famous was great for treating famous people. 2. Ambulance drivers just took you to the hospital. 3. Ambulance drivers knew Dr Famous.

Output: 2

Input: Statement: The union has about 4000 members in Canada. Choices: 1. There's around 4000 Canadians in the union. 2. Toronto has Canada's biggest concentration of union members. 3. There are 100 members in the union that live in Canada.

Output: 3

Input: Statement: First to arrive was the Tang clan, which established a number of walled villages in the New Territories that still exist today. Choices: 1. The clan was not known for building. 2. Before the arrival of the Tang clan, the area was not inhabited by anyone. 3. Thanks to the Tang clan, there are a lot of walled villages that exist today.

Output: 1

Input: Statement: isn't it awful Choices: 1. This isn't too bad. 2. It's not as bad as past situations. 3. That is horrific.

Output: 2

Input: Statement: Construction of the offices obliterated much of the archaeological dig which had unearthed the original layout of the ninth-century quay, but the Viking artifacts that were found are on view in the National Museum  and in the Viking Adventure exhibit. Choices:  1. The Viking Adventure exhibit displays Thor's hammer. 2. The National museum has Viking artifacts on display. 3. Construction of the offices was permanently halted when Viking artifacts were found.

Output: The Viking Adventure exhibit displays Thor's hammer.

Input: Statement: You there Rennie! Drew saw the involuntary spasm of Don Cazar's lips, the shadow of an expression which might mean he anticipated a distasteful scene to come. Choices:  1. Drew knew Don Cazar's expression well because he has known him for a long time. 2. Drew knew Don Quixote. 3. Drew knew Don Cazar's expression well.

Output: 3


Explanation: Commonsense Reasoning

Input: Statement: Next to the MGM Grand you will find M and M World, four stories of merchandise and memorabilia dedicated to the candy that doesn't melt in your hand. Choices: 1. The candy has many fans who love its attractions. 2. There's four stories of memorabilia dedicated to a candy. 3. That particular candy melts and becomes difficult to eat.

Output: 2

Input: Statment: I've forgotten his name now, confessed Tuppence. Choices: 1. Tuppence forgot his name. 2.Tuppence remembered his name later. 3. Tuppence never could forget his name.

Output: 1

Input: Statement: One of the first organizational realignments taking place is in the Office of the Taxpayer Advocate. Choices: 1. The office of the taxpayer advocate is the last to be realigned. 2. The realignment is taking place over a few weeks. 3. The office of the taxpayer advocate is having an organizational realignment.

Output: 3

Input: Statement: yeah i tell you what though if you go price some of those tennis shoes i can see why now you know they're getting up in the hundred dollar range. Choices: 1. The tennis shoes have a range of prices. 2. The tennis shoes can be in the hundred dollar range. 3. The tennis shoes are not over hundred dollars.

Output: 2

Input: Statement: In 1994, fraction of households owning a personal computer is 0.25. Choices: 1. In 1994, 0.25 of households owned a PC, which was a high sum at the time. 2. In 1984, 0.8 of households owned a PC. In 1994, 0.25 of households owned a PC.

Output: 1

Input: Statement: You could say you were in an auto accident, and the ambulance driver took you straight to Dr. Famous' office. Choices: 1. Ambulance drivers knew Dr Famous was great for treating famous people. 2. Ambulance drivers just took you to the hospital. 3. Ambulance drivers knew Dr Famous.

Output: 2

Input: Statement: The union has about 4000 members in Canada. Choices: 1. There's around 4000 Canadians in the union. 2. Toronto has Canada's biggest concentration of union members. 3. There are 100 members in the union that live in Canada.

Output: 3

Input: Statement: First to arrive was the Tang clan, which established a number of walled villages in the New Territories that still exist today. Choices: 1. The clan was not known for building. 2. Before the arrival of the Tang clan, the area was not inhabited by anyone. 3. Thanks to the Tang clan, there are a lot of walled villages that exist today.

Output: 1

Input: Statement: isn't it awful Choices: 1. This isn't too bad. 2. It's not as bad as past situations. 3. That is horrific.

Output: 2

Input: Statement: Construction of the offices obliterated much of the archaeological dig which had unearthed the original layout of the ninth-century quay, but the Viking artifacts that were found are on view in the National Museum  and in the Viking Adventure exhibit. Choices:  1. The Viking Adventure exhibit displays Thor's hammer. 2. The National museum has Viking artifacts on display. 3. Construction of the offices was permanently halted when Viking artifacts were found.

Output: The Viking Adventure exhibit displays Thor's hammer.

Input: Statement: Each June, the Malaysian Nature Society supports an international bird race, where teams compete to identify the largest number of birds. Choices:  1. There is an international bird race every year. 2. There is a special prize for one of the rarest birds. 3. Each June, the Malaysian Nature Society has shunned the international bird race.

Output: 1


Explanation: Commonsense Reasoning

Input: Statement: Next to the MGM Grand you will find M and M World, four stories of merchandise and memorabilia dedicated to the candy that doesn't melt in your hand. Choices: 1. The candy has many fans who love its attractions. 2. There's four stories of memorabilia dedicated to a candy. 3. That particular candy melts and becomes difficult to eat.

Output: 2

Input: Statment: I've forgotten his name now, confessed Tuppence. Choices: 1. Tuppence forgot his name. 2.Tuppence remembered his name later. 3. Tuppence never could forget his name.

Output: 1

Input: Statement: One of the first organizational realignments taking place is in the Office of the Taxpayer Advocate. Choices: 1. The office of the taxpayer advocate is the last to be realigned. 2. The realignment is taking place over a few weeks. 3. The office of the taxpayer advocate is having an organizational realignment.

Output: 3

Input: Statement: yeah i tell you what though if you go price some of those tennis shoes i can see why now you know they're getting up in the hundred dollar range. Choices: 1. The tennis shoes have a range of prices. 2. The tennis shoes can be in the hundred dollar range. 3. The tennis shoes are not over hundred dollars.

Output: 2

Input: Statement: In 1994, fraction of households owning a personal computer is 0.25. Choices: 1. In 1994, 0.25 of households owned a PC, which was a high sum at the time. 2. In 1984, 0.8 of households owned a PC. In 1994, 0.25 of households owned a PC.

Output: 1

Input: Statement: You could say you were in an auto accident, and the ambulance driver took you straight to Dr. Famous' office. Choices: 1. Ambulance drivers knew Dr Famous was great for treating famous people. 2. Ambulance drivers just took you to the hospital. 3. Ambulance drivers knew Dr Famous.

Output: 2

Input: Statement: The union has about 4000 members in Canada. Choices: 1. There's around 4000 Canadians in the union. 2. Toronto has Canada's biggest concentration of union members. 3. There are 100 members in the union that live in Canada.

Output: 3

Input: Statement: First to arrive was the Tang clan, which established a number of walled villages in the New Territories that still exist today. Choices: 1. The clan was not known for building. 2. Before the arrival of the Tang clan, the area was not inhabited by anyone. 3. Thanks to the Tang clan, there are a lot of walled villages that exist today.

Output: 1

Input: Statement: isn't it awful Choices: 1. This isn't too bad. 2. It's not as bad as past situations. 3. That is horrific.

Output: 2

Input: Statement: Construction of the offices obliterated much of the archaeological dig which had unearthed the original layout of the ninth-century quay, but the Viking artifacts that were found are on view in the National Museum  and in the Viking Adventure exhibit. Choices:  1. The Viking Adventure exhibit displays Thor's hammer. 2. The National museum has Viking artifacts on display. 3. Construction of the offices was permanently halted when Viking artifacts were found.

Output: The Viking Adventure exhibit displays Thor's hammer.

Input: Statement: Some states have established insurance-buying groups that employers can join. Choices:  1. Employers will not be able to join state established insurance-buying groups. 2. Employers can join state established insurance-buying groups. 3. Almost all of the states have established at least one insurance-buying group.

Output: 2


Explanation: Commonsense Reasoning

Input: Statement: Next to the MGM Grand you will find M and M World, four stories of merchandise and memorabilia dedicated to the candy that doesn't melt in your hand. Choices: 1. The candy has many fans who love its attractions. 2. There's four stories of memorabilia dedicated to a candy. 3. That particular candy melts and becomes difficult to eat.

Output: 2

Input: Statment: I've forgotten his name now, confessed Tuppence. Choices: 1. Tuppence forgot his name. 2.Tuppence remembered his name later. 3. Tuppence never could forget his name.

Output: 1

Input: Statement: One of the first organizational realignments taking place is in the Office of the Taxpayer Advocate. Choices: 1. The office of the taxpayer advocate is the last to be realigned. 2. The realignment is taking place over a few weeks. 3. The office of the taxpayer advocate is having an organizational realignment.

Output: 3

Input: Statement: yeah i tell you what though if you go price some of those tennis shoes i can see why now you know they're getting up in the hundred dollar range. Choices: 1. The tennis shoes have a range of prices. 2. The tennis shoes can be in the hundred dollar range. 3. The tennis shoes are not over hundred dollars.

Output: 2

Input: Statement: In 1994, fraction of households owning a personal computer is 0.25. Choices: 1. In 1994, 0.25 of households owned a PC, which was a high sum at the time. 2. In 1984, 0.8 of households owned a PC. In 1994, 0.25 of households owned a PC.

Output: 1

Input: Statement: You could say you were in an auto accident, and the ambulance driver took you straight to Dr. Famous' office. Choices: 1. Ambulance drivers knew Dr Famous was great for treating famous people. 2. Ambulance drivers just took you to the hospital. 3. Ambulance drivers knew Dr Famous.

Output: 2

Input: Statement: The union has about 4000 members in Canada. Choices: 1. There's around 4000 Canadians in the union. 2. Toronto has Canada's biggest concentration of union members. 3. There are 100 members in the union that live in Canada.

Output: 3

Input: Statement: First to arrive was the Tang clan, which established a number of walled villages in the New Territories that still exist today. Choices: 1. The clan was not known for building. 2. Before the arrival of the Tang clan, the area was not inhabited by anyone. 3. Thanks to the Tang clan, there are a lot of walled villages that exist today.

Output: 1

Input: Statement: isn't it awful Choices: 1. This isn't too bad. 2. It's not as bad as past situations. 3. That is horrific.

Output: 2

Input: Statement: Construction of the offices obliterated much of the archaeological dig which had unearthed the original layout of the ninth-century quay, but the Viking artifacts that were found are on view in the National Museum  and in the Viking Adventure exhibit. Choices:  1. The Viking Adventure exhibit displays Thor's hammer. 2. The National museum has Viking artifacts on display. 3. Construction of the offices was permanently halted when Viking artifacts were found.

Output: The Viking Adventure exhibit displays Thor's hammer.

Input: Statement: However, young and old, stranger and Dubliner rub shoulders quite happily. Choices:  1. Dubliners hate outsiders, with a particular contempt for the old.  2. Dubliners and strangers get along well regardless of age.  3. Young and old, stranger and Dubliner give one another shoulder massages.

Output: 2


You are given a conversation between two people. 'Person1:' and 'Person2:' are used to separate their respective dialogues. If the conversation begins with a question, label it '1' otherwise '0'.

Input:
Person1:     Passport , please , madam .
 Person2: Just a minute , please . It's in my bag . Here it is .
 Person1:     Thank you . Please fill out the Baggage Declaration Form .
 Person2: All right . Shall I enter all my belongings ?
 Person1:     No , only the articles listed on the Declaration .

Output: 0

Explanation: The first sentence in the conversation begins with a request, therefore there is no presence of a question.

Input:
 Person1:     Hi ! What are you watching ?
 Person2:  It ’ s a program about islam . It ’ s very interesting .
 Person1:      Wow ! So many people ! Where are they and what are they doing ?
 Person2:  They are muslims on a pilgrimage to mecca . Muslims call this pilgrimage “ haj ” .
 Person1:      Why do they go there ?
 Person2:  Muslims believe that every man who is able should go on a haj at least once in his life . Mecca is the spiritual centre of the muslim faith .
 Person1:      When muslims pray , they face towards mecca .
 Person2:  That ’ s right . Unfortunately , so many people go on the haj each year that there are often stamped and people get killed .
 Person1:      I heard about that . The pilgrims must walk around a large , sacred black stone .
 Person2:  That ’ s right . That ’ s when accidents often happen . The Saudi government tries to limit the number of pilgrims , to reduce the chances of accidents .
 Person1:      Pilgrimages are common in many faiths .
 Person2:  Yes . In England , Christian pilgrims might go to Canterbury and many Christians go to the Vatican on pilgrimages .
 Person1:      Isn ’ t there a place in france where people go to get healed ?
 Person2:  I think that place is Lourdes . There are many stories of people being healed after visiting there .
 Person1:      Do you think that there is something magical about that place ?
 Person2:  Personally . I think that people believe they will be healed and that faith causes a change in their mind that cures them . I don ’ t think place is magical in any way .

Output: 1

Explanation: The first sentence contains a question, therefore the output is correctly mentioned as 1

Input:
Person1:     You look terrific and in good shape .
 Person2: Thank you .
 Person1:     How do you keep fit ?
 Person2:  I do yoga twice a weeks
 Person1:     Yoga ? It sounds interesting .
 Person2: It was hard at the beginning , but now feel it's really relaxing . It makes me flexible .
 Person1:     Anything else ?
 Person2: Yes , I often go swimming .
 Person1:     Are you interested in swimming ?
 Person2: Yes , I like it very much .

Output: 0

Explanation: The first sentence is not a question, therefore the output is correct.

Input:
Person1:     Can I borrow five bucks ?
 Person2:  No !
 Person1:     Come on ! I'll pay you back on Tuesday .
 Person2: Last time I lent you money , you never paid me back .
 Person1:     I promise if you lend me five dollars today , I will repay you in full next week .
 Person2: Ok , but I'm taking your skateboard as collateral .
 Person1:     Fine ! I can't believe you don't trust me .
 Person2: It's nothing personal , just business .

Output: 0

Explanation: The first question does begin with a question, therefore the classification result must be 1 instead of 0.

Input:
Person1:     I ’ m so surprised to meet you in this remote village . Long time no see .
 Person2: Yeah . How are you these days ?

Output: 1

Explanation: The first sentence contains a statement about the remote village, since it is not a question the output should be 0 instead of 1.

Input:
 Person1:     Guess what ? Paul and Susan are engaged .
 Person2:  Really ? When did that happen ?
 Person1:      A week ago ? They met last summer and now just sink . They will be married soon .
 Person2:  Have they set a date for the wedding ?
 Person1:      No , not yet . But Susan says they ’ d like to get married in November or December . Then they ’ ll go to Hawaii for their honeymoon .

Output: 1


Input:
Person1:     Passport , please , madam .
 Person2: Just a minute , please . It's in my bag . Here it is .
 Person1:     Thank you . Please fill out the Baggage Declaration Form .
 Person2: All right . Shall I enter all my belongings ?
 Person1:     No , only the articles listed on the Declaration .

Output: 0

Input:
 Person1:     Hi ! What are you watching ?
 Person2:  It ’ s a program about islam . It ’ s very interesting .
 Person1:      Wow ! So many people ! Where are they and what are they doing ?
 Person2:  They are muslims on a pilgrimage to mecca . Muslims call this pilgrimage “ haj ” .
 Person1:      Why do they go there ?
 Person2:  Muslims believe that every man who is able should go on a haj at least once in his life . Mecca is the spiritual centre of the muslim faith .
 Person1:      When muslims pray , they face towards mecca .
 Person2:  That ’ s right . Unfortunately , so many people go on the haj each year that there are often stamped and people get killed .
 Person1:      I heard about that . The pilgrims must walk around a large , sacred black stone .
 Person2:  That ’ s right . That ’ s when accidents often happen . The Saudi government tries to limit the number of pilgrims , to reduce the chances of accidents .
 Person1:      Pilgrimages are common in many faiths .
 Person2:  Yes . In England , Christian pilgrims might go to Canterbury and many Christians go to the Vatican on pilgrimages .
 Person1:      Isn ’ t there a place in france where people go to get healed ?
 Person2:  I think that place is Lourdes . There are many stories of people being healed after visiting there .
 Person1:      Do you think that there is something magical about that place ?
 Person2:  Personally . I think that people believe they will be healed and that faith causes a change in their mind that cures them . I don ’ t think place is magical in any way .

Output: 1

Input:
Person1:     You look terrific and in good shape .
 Person2: Thank you .
 Person1:     How do you keep fit ?
 Person2:  I do yoga twice a weeks
 Person1:     Yoga ? It sounds interesting .
 Person2: It was hard at the beginning , but now feel it's really relaxing . It makes me flexible .
 Person1:     Anything else ?
 Person2: Yes , I often go swimming .
 Person1:     Are you interested in swimming ?
 Person2: Yes , I like it very much .

Output: 0

Input:
Person1:     Can I borrow five bucks ?
 Person2:  No !
 Person1:     Come on ! I'll pay you back on Tuesday .
 Person2: Last time I lent you money , you never paid me back .
 Person1:     I promise if you lend me five dollars today , I will repay you in full next week .
 Person2: Ok , but I'm taking your skateboard as collateral .
 Person1:     Fine ! I can't believe you don't trust me .
 Person2: It's nothing personal , just business .

Output: 0

Input:
Person1:     I ’ m so surprised to meet you in this remote village . Long time no see .
 Person2: Yeah . How are you these days ?

Output: 1

Input:
 Person1:     Awful ! My cell phone is out of service again . Fred , can you lend me some money ?
 Person2:  How much do you want ?
 Person1:      200yuan . I bought a computer online , so I have to pay the mortgage .
 Person2:  I am so sorry to hear that . Is that enough ?
 Person1:      It's so nice of you . You really make my day .
 Person2:  Forget it ! We seemingly need to gather money little by little .
 Person1:      What you said is absolutely right . I will pay you back as soon as I get back on my feet .
 Person2:  No rush . You can give it back whenever you have it .

Output: 0


Input:
Person1:     Passport , please , madam .
 Person2: Just a minute , please . It's in my bag . Here it is .
 Person1:     Thank you . Please fill out the Baggage Declaration Form .
 Person2: All right . Shall I enter all my belongings ?
 Person1:     No , only the articles listed on the Declaration .

Output: 0

Input:
 Person1:     Hi ! What are you watching ?
 Person2:  It ’ s a program about islam . It ’ s very interesting .
 Person1:      Wow ! So many people ! Where are they and what are they doing ?
 Person2:  They are muslims on a pilgrimage to mecca . Muslims call this pilgrimage “ haj ” .
 Person1:      Why do they go there ?
 Person2:  Muslims believe that every man who is able should go on a haj at least once in his life . Mecca is the spiritual centre of the muslim faith .
 Person1:      When muslims pray , they face towards mecca .
 Person2:  That ’ s right . Unfortunately , so many people go on the haj each year that there are often stamped and people get killed .
 Person1:      I heard about that . The pilgrims must walk around a large , sacred black stone .
 Person2:  That ’ s right . That ’ s when accidents often happen . The Saudi government tries to limit the number of pilgrims , to reduce the chances of accidents .
 Person1:      Pilgrimages are common in many faiths .
 Person2:  Yes . In England , Christian pilgrims might go to Canterbury and many Christians go to the Vatican on pilgrimages .
 Person1:      Isn ’ t there a place in france where people go to get healed ?
 Person2:  I think that place is Lourdes . There are many stories of people being healed after visiting there .
 Person1:      Do you think that there is something magical about that place ?
 Person2:  Personally . I think that people believe they will be healed and that faith causes a change in their mind that cures them . I don ’ t think place is magical in any way .

Output: 1

Input:
Person1:     You look terrific and in good shape .
 Person2: Thank you .
 Person1:     How do you keep fit ?
 Person2:  I do yoga twice a weeks
 Person1:     Yoga ? It sounds interesting .
 Person2: It was hard at the beginning , but now feel it's really relaxing . It makes me flexible .
 Person1:     Anything else ?
 Person2: Yes , I often go swimming .
 Person1:     Are you interested in swimming ?
 Person2: Yes , I like it very much .

Output: 0

Input:
Person1:     Can I borrow five bucks ?
 Person2:  No !
 Person1:     Come on ! I'll pay you back on Tuesday .
 Person2: Last time I lent you money , you never paid me back .
 Person1:     I promise if you lend me five dollars today , I will repay you in full next week .
 Person2: Ok , but I'm taking your skateboard as collateral .
 Person1:     Fine ! I can't believe you don't trust me .
 Person2: It's nothing personal , just business .

Output: 0

Input:
Person1:     I ’ m so surprised to meet you in this remote village . Long time no see .
 Person2: Yeah . How are you these days ?

Output: 1

Input:
 Person1:     I'll never ever work together with him !
 Person2:  What happened ? Quarrel again ?
 Person1:      Will you take my materials back from him for me ? Please !
 Person2:  Don't ask me . I don't wanna be involved in your quarrel .

Output: 0


Input:
Person1:     Passport , please , madam .
 Person2: Just a minute , please . It's in my bag . Here it is .
 Person1:     Thank you . Please fill out the Baggage Declaration Form .
 Person2: All right . Shall I enter all my belongings ?
 Person1:     No , only the articles listed on the Declaration .

Output: 0

Input:
 Person1:     Hi ! What are you watching ?
 Person2:  It ’ s a program about islam . It ’ s very interesting .
 Person1:      Wow ! So many people ! Where are they and what are they doing ?
 Person2:  They are muslims on a pilgrimage to mecca . Muslims call this pilgrimage “ haj ” .
 Person1:      Why do they go there ?
 Person2:  Muslims believe that every man who is able should go on a haj at least once in his life . Mecca is the spiritual centre of the muslim faith .
 Person1:      When muslims pray , they face towards mecca .
 Person2:  That ’ s right . Unfortunately , so many people go on the haj each year that there are often stamped and people get killed .
 Person1:      I heard about that . The pilgrims must walk around a large , sacred black stone .
 Person2:  That ’ s right . That ’ s when accidents often happen . The Saudi government tries to limit the number of pilgrims , to reduce the chances of accidents .
 Person1:      Pilgrimages are common in many faiths .
 Person2:  Yes . In England , Christian pilgrims might go to Canterbury and many Christians go to the Vatican on pilgrimages .
 Person1:      Isn ’ t there a place in france where people go to get healed ?
 Person2:  I think that place is Lourdes . There are many stories of people being healed after visiting there .
 Person1:      Do you think that there is something magical about that place ?
 Person2:  Personally . I think that people believe they will be healed and that faith causes a change in their mind that cures them . I don ’ t think place is magical in any way .

Output: 1

Input:
Person1:     You look terrific and in good shape .
 Person2: Thank you .
 Person1:     How do you keep fit ?
 Person2:  I do yoga twice a weeks
 Person1:     Yoga ? It sounds interesting .
 Person2: It was hard at the beginning , but now feel it's really relaxing . It makes me flexible .
 Person1:     Anything else ?
 Person2: Yes , I often go swimming .
 Person1:     Are you interested in swimming ?
 Person2: Yes , I like it very much .

Output: 0

Input:
Person1:     Can I borrow five bucks ?
 Person2:  No !
 Person1:     Come on ! I'll pay you back on Tuesday .
 Person2: Last time I lent you money , you never paid me back .
 Person1:     I promise if you lend me five dollars today , I will repay you in full next week .
 Person2: Ok , but I'm taking your skateboard as collateral .
 Person1:     Fine ! I can't believe you don't trust me .
 Person2: It's nothing personal , just business .

Output: 0

Input:
Person1:     I ’ m so surprised to meet you in this remote village . Long time no see .
 Person2: Yeah . How are you these days ?

Output: 1

Input:
 Person1:     Where do you want to go ?
 Person2:  I am going to the Capital Hotel .
 Person1:      Get in . I will take you there .
 Person2:  About how much it will cost ?
 Person1:      $ 50 .
 Person2:  On the meter ?
 Person1:      Ye , of course .
 Person2:  OK.let ' s go .

Output: 1


Input:
Person1:     Passport , please , madam .
 Person2: Just a minute , please . It's in my bag . Here it is .
 Person1:     Thank you . Please fill out the Baggage Declaration Form .
 Person2: All right . Shall I enter all my belongings ?
 Person1:     No , only the articles listed on the Declaration .

Output: 0

Input:
 Person1:     Hi ! What are you watching ?
 Person2:  It ’ s a program about islam . It ’ s very interesting .
 Person1:      Wow ! So many people ! Where are they and what are they doing ?
 Person2:  They are muslims on a pilgrimage to mecca . Muslims call this pilgrimage “ haj ” .
 Person1:      Why do they go there ?
 Person2:  Muslims believe that every man who is able should go on a haj at least once in his life . Mecca is the spiritual centre of the muslim faith .
 Person1:      When muslims pray , they face towards mecca .
 Person2:  That ’ s right . Unfortunately , so many people go on the haj each year that there are often stamped and people get killed .
 Person1:      I heard about that . The pilgrims must walk around a large , sacred black stone .
 Person2:  That ’ s right . That ’ s when accidents often happen . The Saudi government tries to limit the number of pilgrims , to reduce the chances of accidents .
 Person1:      Pilgrimages are common in many faiths .
 Person2:  Yes . In England , Christian pilgrims might go to Canterbury and many Christians go to the Vatican on pilgrimages .
 Person1:      Isn ’ t there a place in france where people go to get healed ?
 Person2:  I think that place is Lourdes . There are many stories of people being healed after visiting there .
 Person1:      Do you think that there is something magical about that place ?
 Person2:  Personally . I think that people believe they will be healed and that faith causes a change in their mind that cures them . I don ’ t think place is magical in any way .

Output: 1

Input:
Person1:     You look terrific and in good shape .
 Person2: Thank you .
 Person1:     How do you keep fit ?
 Person2:  I do yoga twice a weeks
 Person1:     Yoga ? It sounds interesting .
 Person2: It was hard at the beginning , but now feel it's really relaxing . It makes me flexible .
 Person1:     Anything else ?
 Person2: Yes , I often go swimming .
 Person1:     Are you interested in swimming ?
 Person2: Yes , I like it very much .

Output: 0

Input:
Person1:     Can I borrow five bucks ?
 Person2:  No !
 Person1:     Come on ! I'll pay you back on Tuesday .
 Person2: Last time I lent you money , you never paid me back .
 Person1:     I promise if you lend me five dollars today , I will repay you in full next week .
 Person2: Ok , but I'm taking your skateboard as collateral .
 Person1:     Fine ! I can't believe you don't trust me .
 Person2: It's nothing personal , just business .

Output: 0

Input:
Person1:     I ’ m so surprised to meet you in this remote village . Long time no see .
 Person2: Yeah . How are you these days ?

Output: 1

Input:
 Person1:     The stepmother was very wicked . She couldn't stand a happy smile on the boy's face .
 Person2:  And ?
 Person1:      And she did her best to pull out the thorn in her flesh and the boy lost whatever made him happy .
 Person2:  Poor child !

Output: 0


In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Input: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.

Output: A Neural Local Coherence Model

Explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage

Input: We present four methods for recovering the epipolar geometry from images of smooth surfaces. In the existing methods for recovering epipolar geometry corresponding feature points are used that cannot be found in such images. The first method is based on finding corresponding characteristic points created by illumination (ICPM illumination characteristic points method). The second method is based on correspondent tangency points created by tangents from epipoles to outline of smooth bodies (OTPM outline tangent points method). These two methods are exact and give correct results for real images, because positions of the corresponding illumination characteristic points and corresponding outline are known with small errors. But the second method is limited either to special type of scenes or to restricted camera motion. We also consider two more methods which are termed CCPM (curve characteristic points method, green curves are used for this method on Figures) and CTPM (curve tangent points method, red curves are used for this method on Figures), for searching epipolar geometry for images of smooth bodies based on a set of level curves (isophoto curves) with a constant illumination intensity. The CCPM method is based on searching correspondent points on isophoto curves with the help of correlation of curvatures between these lines. The CTPM method is based on property of the tangential to isophoto curve epipolar line to map into the tangential to correspondent isophoto curves epipolar line. The standard method termed SM (standard method, blue curves are used for this method on Figures) and based on knowledge of pairs of the almost exact correspondent points, has been used for testing of these two methods. The main technical contributions of our CCPM method are following. The first of them consists of bounding the search space for epipole locations. On the face of it, this space is infinite and unbounded. We suggest a method to partition the infinite plane into a finite number of regions. This partition is based on the desired accuracy and maintains properties that yield an efficient search over the infinite plane. The second is an efficient method for finding correspondence between points of two closed isophoto curves and finding homography, mapping between these two isophoto curves. Then this homography is corrected for all possible epipole positions with the help of evaluation function. A finite subset of solution is chosen from the full set given by all possible epipole positions. This subset includes fundamental matrices giving local minimums of evaluating function close to global minimum. Epipoles of this subset lie almost on straight line directed parallel to parallax shift. CTPM method was used to find the best solution from this subset. Our method is applicable to any pair of images of smooth objects taken under perspective projection models, as long as assumption of the constant brightness is taken for granted. The methods have

Output: Recovering Epipolar Geometry from Images of Smooth Surfaces

Explanation: This statement "Recovering Epipolar Geometry from Images of Smooth Surfaces" is taken from the part of the passage "recovering the epipolar geometry" and its process description in the passage. This is a positive example because the title indicates the description mentioned in the passage.

Input: Word sense disambiguation (WSD) is a problem in the field of computational linguistics given as finding the intended sense of a word (or a set of words) when it is activated within a certain context. WSD was recently addressed as a combinatorial optimization problem in which the goal is to find a sequence of senses that maximize the semantic relatedness among the target words. In this article, a novel algorithm for solving the WSD problem called D-Bees is proposed which is inspired by bee colony optimization (BCO) where artificial bee agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a standard dataset (SemEval 2007 coarse-grained English all-words task corpus) and is compared to simulated annealing, genetic algorithms, and two ant colony optimization techniques (ACO). It will be observed that the BCO and ACO approaches are on par. 1 ar X iv :1 40 5. 14 06 v1 [ cs .C L ] 6 M ay 2 01 4

Output: D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation

Explanation: This statement "D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation" is an inference from the part of the passage " a novel algorithm for solving the WSD problem called D-Bees is proposed which is inspired by bee colony optimization (BCO)" and its description in the passage. This is a positive example because the title references to the explanation mentioned in the passage.

Input: Semantic role labelling (SRL) is a task in natural language processing which detects and classifies the semantic arguments associated with the predicates of a sentence. It is an important step towards understanding the meaning of a natural language. There exists SRL systems for well-studied languages like English, Chinese or Japanese but there is not any such system for the Vietnamese language. In this paper, we present the first SRL system for Vietnamese with encouraging accuracy. We first demonstrate that a simple application of SRL techniques developed for English could not give a good accuracy for Vietnamese. We then introduce a new algorithm for extracting candidate syntactic constituents, which is much more accurate than the common node-mapping algorithm usually used in the identification step. Finally, in the classification step, in addition to the common linguistic features, we propose novel and useful features for use in SRL. Our SRL system achieves an F1 score of 73.53% on the Vietnamese PropBank corpus. This system, including software and corpus, is available as an open source project and we believe that it is a good baseline for the development of future Vietnamese SRL systems.

Output: History of Vietnamese language

Explanation: The statement "History of Vietnamese language" is a negative example because it does not infer from the passage. It indicates that the passage is about the history of Vietnamese language but is infact about Semantic role labelling and its importance.

Input: This article presents the top-level of an ontology categorizing and generalizing best practices and quality criteria or measures for Linked Data. It permits to compare these techniques and have a synthetic organized view of what can or should be done for knowledge sharing purposes. This ontology is part of a general knowledge base that can be accessed and complemented by any Web user. Thus, it can be seen as a cooperatively built library for the above cited elements. Since they permit to evaluate information objects and create better ones, these elements also permit knowledgebased tools and techniques – as well as knowledge providers – to be evaluated and categorized based on their input/output information objects. One top-level distinction permitting to organize this ontology is the one between content, medium and containers of descriptions. Various structural, ontological, syntactical and lexical distinctions are then used.

Output: Organization of Data

Explanation: This statement "Organization of Lined Data" indicates that the passage is about ways to organize data. This is a negative example because the passage informs about ontology categrizing and generalizing and not about organization of data.

Input: Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames.

Output: Video Description using Bidirectional Recurrent Neural Networks


Input: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.

Output: A Neural Local Coherence Model

Input: We present four methods for recovering the epipolar geometry from images of smooth surfaces. In the existing methods for recovering epipolar geometry corresponding feature points are used that cannot be found in such images. The first method is based on finding corresponding characteristic points created by illumination (ICPM illumination characteristic points method). The second method is based on correspondent tangency points created by tangents from epipoles to outline of smooth bodies (OTPM outline tangent points method). These two methods are exact and give correct results for real images, because positions of the corresponding illumination characteristic points and corresponding outline are known with small errors. But the second method is limited either to special type of scenes or to restricted camera motion. We also consider two more methods which are termed CCPM (curve characteristic points method, green curves are used for this method on Figures) and CTPM (curve tangent points method, red curves are used for this method on Figures), for searching epipolar geometry for images of smooth bodies based on a set of level curves (isophoto curves) with a constant illumination intensity. The CCPM method is based on searching correspondent points on isophoto curves with the help of correlation of curvatures between these lines. The CTPM method is based on property of the tangential to isophoto curve epipolar line to map into the tangential to correspondent isophoto curves epipolar line. The standard method termed SM (standard method, blue curves are used for this method on Figures) and based on knowledge of pairs of the almost exact correspondent points, has been used for testing of these two methods. The main technical contributions of our CCPM method are following. The first of them consists of bounding the search space for epipole locations. On the face of it, this space is infinite and unbounded. We suggest a method to partition the infinite plane into a finite number of regions. This partition is based on the desired accuracy and maintains properties that yield an efficient search over the infinite plane. The second is an efficient method for finding correspondence between points of two closed isophoto curves and finding homography, mapping between these two isophoto curves. Then this homography is corrected for all possible epipole positions with the help of evaluation function. A finite subset of solution is chosen from the full set given by all possible epipole positions. This subset includes fundamental matrices giving local minimums of evaluating function close to global minimum. Epipoles of this subset lie almost on straight line directed parallel to parallax shift. CTPM method was used to find the best solution from this subset. Our method is applicable to any pair of images of smooth objects taken under perspective projection models, as long as assumption of the constant brightness is taken for granted. The methods have

Output: Recovering Epipolar Geometry from Images of Smooth Surfaces

Input: Word sense disambiguation (WSD) is a problem in the field of computational linguistics given as finding the intended sense of a word (or a set of words) when it is activated within a certain context. WSD was recently addressed as a combinatorial optimization problem in which the goal is to find a sequence of senses that maximize the semantic relatedness among the target words. In this article, a novel algorithm for solving the WSD problem called D-Bees is proposed which is inspired by bee colony optimization (BCO) where artificial bee agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a standard dataset (SemEval 2007 coarse-grained English all-words task corpus) and is compared to simulated annealing, genetic algorithms, and two ant colony optimization techniques (ACO). It will be observed that the BCO and ACO approaches are on par. 1 ar X iv :1 40 5. 14 06 v1 [ cs .C L ] 6 M ay 2 01 4

Output: D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation

Input: Semantic role labelling (SRL) is a task in natural language processing which detects and classifies the semantic arguments associated with the predicates of a sentence. It is an important step towards understanding the meaning of a natural language. There exists SRL systems for well-studied languages like English, Chinese or Japanese but there is not any such system for the Vietnamese language. In this paper, we present the first SRL system for Vietnamese with encouraging accuracy. We first demonstrate that a simple application of SRL techniques developed for English could not give a good accuracy for Vietnamese. We then introduce a new algorithm for extracting candidate syntactic constituents, which is much more accurate than the common node-mapping algorithm usually used in the identification step. Finally, in the classification step, in addition to the common linguistic features, we propose novel and useful features for use in SRL. Our SRL system achieves an F1 score of 73.53% on the Vietnamese PropBank corpus. This system, including software and corpus, is available as an open source project and we believe that it is a good baseline for the development of future Vietnamese SRL systems.

Output: History of Vietnamese language

Input: This article presents the top-level of an ontology categorizing and generalizing best practices and quality criteria or measures for Linked Data. It permits to compare these techniques and have a synthetic organized view of what can or should be done for knowledge sharing purposes. This ontology is part of a general knowledge base that can be accessed and complemented by any Web user. Thus, it can be seen as a cooperatively built library for the above cited elements. Since they permit to evaluate information objects and create better ones, these elements also permit knowledgebased tools and techniques – as well as knowledge providers – to be evaluated and categorized based on their input/output information objects. One top-level distinction permitting to organize this ontology is the one between content, medium and containers of descriptions. Various structural, ontological, syntactical and lexical distinctions are then used.

Output: Organization of Data

Input: Conventional dependency parsers rely on a statistical model and a transition system or graph algorithm to enforce tree-structured outputs during training and inference. In this work we formalize dependency parsing as the problem of selecting the head (a.k.a. parent) of each word in a sentence. Our model which we call DENSE (as shorthand for Dependency Neural Selection) employs bidirectional recurrent neural networks for the head selection task. Without enforcing any structural constraints during training, DENSE generates (at inference time) trees for the overwhelming majority of sentences (95% on an English dataset), while remaining non-tree outputs can be adjusted with a maximum spanning tree algorithm. We evaluate DENSE on four languages (English, Chinese, Czech, and German) with varying degrees of non-projectivity. Despite the simplicity of our approach, experiments show that the resulting parsers are on par with or outperform the state of the art.

Output: Dependency Parsing as Head Selection


Input: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.

Output: A Neural Local Coherence Model

Input: We present four methods for recovering the epipolar geometry from images of smooth surfaces. In the existing methods for recovering epipolar geometry corresponding feature points are used that cannot be found in such images. The first method is based on finding corresponding characteristic points created by illumination (ICPM illumination characteristic points method). The second method is based on correspondent tangency points created by tangents from epipoles to outline of smooth bodies (OTPM outline tangent points method). These two methods are exact and give correct results for real images, because positions of the corresponding illumination characteristic points and corresponding outline are known with small errors. But the second method is limited either to special type of scenes or to restricted camera motion. We also consider two more methods which are termed CCPM (curve characteristic points method, green curves are used for this method on Figures) and CTPM (curve tangent points method, red curves are used for this method on Figures), for searching epipolar geometry for images of smooth bodies based on a set of level curves (isophoto curves) with a constant illumination intensity. The CCPM method is based on searching correspondent points on isophoto curves with the help of correlation of curvatures between these lines. The CTPM method is based on property of the tangential to isophoto curve epipolar line to map into the tangential to correspondent isophoto curves epipolar line. The standard method termed SM (standard method, blue curves are used for this method on Figures) and based on knowledge of pairs of the almost exact correspondent points, has been used for testing of these two methods. The main technical contributions of our CCPM method are following. The first of them consists of bounding the search space for epipole locations. On the face of it, this space is infinite and unbounded. We suggest a method to partition the infinite plane into a finite number of regions. This partition is based on the desired accuracy and maintains properties that yield an efficient search over the infinite plane. The second is an efficient method for finding correspondence between points of two closed isophoto curves and finding homography, mapping between these two isophoto curves. Then this homography is corrected for all possible epipole positions with the help of evaluation function. A finite subset of solution is chosen from the full set given by all possible epipole positions. This subset includes fundamental matrices giving local minimums of evaluating function close to global minimum. Epipoles of this subset lie almost on straight line directed parallel to parallax shift. CTPM method was used to find the best solution from this subset. Our method is applicable to any pair of images of smooth objects taken under perspective projection models, as long as assumption of the constant brightness is taken for granted. The methods have

Output: Recovering Epipolar Geometry from Images of Smooth Surfaces

Input: Word sense disambiguation (WSD) is a problem in the field of computational linguistics given as finding the intended sense of a word (or a set of words) when it is activated within a certain context. WSD was recently addressed as a combinatorial optimization problem in which the goal is to find a sequence of senses that maximize the semantic relatedness among the target words. In this article, a novel algorithm for solving the WSD problem called D-Bees is proposed which is inspired by bee colony optimization (BCO) where artificial bee agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a standard dataset (SemEval 2007 coarse-grained English all-words task corpus) and is compared to simulated annealing, genetic algorithms, and two ant colony optimization techniques (ACO). It will be observed that the BCO and ACO approaches are on par. 1 ar X iv :1 40 5. 14 06 v1 [ cs .C L ] 6 M ay 2 01 4

Output: D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation

Input: Semantic role labelling (SRL) is a task in natural language processing which detects and classifies the semantic arguments associated with the predicates of a sentence. It is an important step towards understanding the meaning of a natural language. There exists SRL systems for well-studied languages like English, Chinese or Japanese but there is not any such system for the Vietnamese language. In this paper, we present the first SRL system for Vietnamese with encouraging accuracy. We first demonstrate that a simple application of SRL techniques developed for English could not give a good accuracy for Vietnamese. We then introduce a new algorithm for extracting candidate syntactic constituents, which is much more accurate than the common node-mapping algorithm usually used in the identification step. Finally, in the classification step, in addition to the common linguistic features, we propose novel and useful features for use in SRL. Our SRL system achieves an F1 score of 73.53% on the Vietnamese PropBank corpus. This system, including software and corpus, is available as an open source project and we believe that it is a good baseline for the development of future Vietnamese SRL systems.

Output: History of Vietnamese language

Input: This article presents the top-level of an ontology categorizing and generalizing best practices and quality criteria or measures for Linked Data. It permits to compare these techniques and have a synthetic organized view of what can or should be done for knowledge sharing purposes. This ontology is part of a general knowledge base that can be accessed and complemented by any Web user. Thus, it can be seen as a cooperatively built library for the above cited elements. Since they permit to evaluate information objects and create better ones, these elements also permit knowledgebased tools and techniques – as well as knowledge providers – to be evaluated and categorized based on their input/output information objects. One top-level distinction permitting to organize this ontology is the one between content, medium and containers of descriptions. Various structural, ontological, syntactical and lexical distinctions are then used.

Output: Organization of Data

Input: We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain their previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward stochastic depth networks. We perform an empirical investigation of various RNN regularizers, and find encouraging results: zoneout gives significant performance improvements across tasks, yielding state-ofthe-art results in character-level language modeling on the Penn Treebank dataset and competitive results on word-level Penn Treebank and permuted sequential MNIST classification tasks.

Output: Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations


Input: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.

Output: A Neural Local Coherence Model

Input: We present four methods for recovering the epipolar geometry from images of smooth surfaces. In the existing methods for recovering epipolar geometry corresponding feature points are used that cannot be found in such images. The first method is based on finding corresponding characteristic points created by illumination (ICPM illumination characteristic points method). The second method is based on correspondent tangency points created by tangents from epipoles to outline of smooth bodies (OTPM outline tangent points method). These two methods are exact and give correct results for real images, because positions of the corresponding illumination characteristic points and corresponding outline are known with small errors. But the second method is limited either to special type of scenes or to restricted camera motion. We also consider two more methods which are termed CCPM (curve characteristic points method, green curves are used for this method on Figures) and CTPM (curve tangent points method, red curves are used for this method on Figures), for searching epipolar geometry for images of smooth bodies based on a set of level curves (isophoto curves) with a constant illumination intensity. The CCPM method is based on searching correspondent points on isophoto curves with the help of correlation of curvatures between these lines. The CTPM method is based on property of the tangential to isophoto curve epipolar line to map into the tangential to correspondent isophoto curves epipolar line. The standard method termed SM (standard method, blue curves are used for this method on Figures) and based on knowledge of pairs of the almost exact correspondent points, has been used for testing of these two methods. The main technical contributions of our CCPM method are following. The first of them consists of bounding the search space for epipole locations. On the face of it, this space is infinite and unbounded. We suggest a method to partition the infinite plane into a finite number of regions. This partition is based on the desired accuracy and maintains properties that yield an efficient search over the infinite plane. The second is an efficient method for finding correspondence between points of two closed isophoto curves and finding homography, mapping between these two isophoto curves. Then this homography is corrected for all possible epipole positions with the help of evaluation function. A finite subset of solution is chosen from the full set given by all possible epipole positions. This subset includes fundamental matrices giving local minimums of evaluating function close to global minimum. Epipoles of this subset lie almost on straight line directed parallel to parallax shift. CTPM method was used to find the best solution from this subset. Our method is applicable to any pair of images of smooth objects taken under perspective projection models, as long as assumption of the constant brightness is taken for granted. The methods have

Output: Recovering Epipolar Geometry from Images of Smooth Surfaces

Input: Word sense disambiguation (WSD) is a problem in the field of computational linguistics given as finding the intended sense of a word (or a set of words) when it is activated within a certain context. WSD was recently addressed as a combinatorial optimization problem in which the goal is to find a sequence of senses that maximize the semantic relatedness among the target words. In this article, a novel algorithm for solving the WSD problem called D-Bees is proposed which is inspired by bee colony optimization (BCO) where artificial bee agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a standard dataset (SemEval 2007 coarse-grained English all-words task corpus) and is compared to simulated annealing, genetic algorithms, and two ant colony optimization techniques (ACO). It will be observed that the BCO and ACO approaches are on par. 1 ar X iv :1 40 5. 14 06 v1 [ cs .C L ] 6 M ay 2 01 4

Output: D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation

Input: Semantic role labelling (SRL) is a task in natural language processing which detects and classifies the semantic arguments associated with the predicates of a sentence. It is an important step towards understanding the meaning of a natural language. There exists SRL systems for well-studied languages like English, Chinese or Japanese but there is not any such system for the Vietnamese language. In this paper, we present the first SRL system for Vietnamese with encouraging accuracy. We first demonstrate that a simple application of SRL techniques developed for English could not give a good accuracy for Vietnamese. We then introduce a new algorithm for extracting candidate syntactic constituents, which is much more accurate than the common node-mapping algorithm usually used in the identification step. Finally, in the classification step, in addition to the common linguistic features, we propose novel and useful features for use in SRL. Our SRL system achieves an F1 score of 73.53% on the Vietnamese PropBank corpus. This system, including software and corpus, is available as an open source project and we believe that it is a good baseline for the development of future Vietnamese SRL systems.

Output: History of Vietnamese language

Input: This article presents the top-level of an ontology categorizing and generalizing best practices and quality criteria or measures for Linked Data. It permits to compare these techniques and have a synthetic organized view of what can or should be done for knowledge sharing purposes. This ontology is part of a general knowledge base that can be accessed and complemented by any Web user. Thus, it can be seen as a cooperatively built library for the above cited elements. Since they permit to evaluate information objects and create better ones, these elements also permit knowledgebased tools and techniques – as well as knowledge providers – to be evaluated and categorized based on their input/output information objects. One top-level distinction permitting to organize this ontology is the one between content, medium and containers of descriptions. Various structural, ontological, syntactical and lexical distinctions are then used.

Output: Organization of Data

Input: The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale, complex applications, incomplete DCOP algorithms are necessary. Current incomplete DCOP algorithms suffer of one or more of the following limitations: they (a) find local minima without providing quality guarantees; (b) provide loose quality assessment; or (c) are unable to benefit from the structure of the problem, such as domain-dependent knowledge and hard constraints. Therefore, capitalizing on strategies from the centralized constraint solving community, we propose a Distributed Large Neighborhood Search (D-LNS) framework to solve DCOPs. The proposed framework (with its novel repair phase) provides guarantees on solution quality, refining upper and lower bounds during the iterative process, and can exploit domain-dependent structures. Our experimental results show that D-LNS outperforms other incomplete DCOP algorithms on both structured and unstructured problem instances.

Output: Solving DCOPs with Distributed Large Neighborhood Search

Distributed Large Neighborhood Search for Distributed Cons


Input: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.

Output: A Neural Local Coherence Model

Input: We present four methods for recovering the epipolar geometry from images of smooth surfaces. In the existing methods for recovering epipolar geometry corresponding feature points are used that cannot be found in such images. The first method is based on finding corresponding characteristic points created by illumination (ICPM illumination characteristic points method). The second method is based on correspondent tangency points created by tangents from epipoles to outline of smooth bodies (OTPM outline tangent points method). These two methods are exact and give correct results for real images, because positions of the corresponding illumination characteristic points and corresponding outline are known with small errors. But the second method is limited either to special type of scenes or to restricted camera motion. We also consider two more methods which are termed CCPM (curve characteristic points method, green curves are used for this method on Figures) and CTPM (curve tangent points method, red curves are used for this method on Figures), for searching epipolar geometry for images of smooth bodies based on a set of level curves (isophoto curves) with a constant illumination intensity. The CCPM method is based on searching correspondent points on isophoto curves with the help of correlation of curvatures between these lines. The CTPM method is based on property of the tangential to isophoto curve epipolar line to map into the tangential to correspondent isophoto curves epipolar line. The standard method termed SM (standard method, blue curves are used for this method on Figures) and based on knowledge of pairs of the almost exact correspondent points, has been used for testing of these two methods. The main technical contributions of our CCPM method are following. The first of them consists of bounding the search space for epipole locations. On the face of it, this space is infinite and unbounded. We suggest a method to partition the infinite plane into a finite number of regions. This partition is based on the desired accuracy and maintains properties that yield an efficient search over the infinite plane. The second is an efficient method for finding correspondence between points of two closed isophoto curves and finding homography, mapping between these two isophoto curves. Then this homography is corrected for all possible epipole positions with the help of evaluation function. A finite subset of solution is chosen from the full set given by all possible epipole positions. This subset includes fundamental matrices giving local minimums of evaluating function close to global minimum. Epipoles of this subset lie almost on straight line directed parallel to parallax shift. CTPM method was used to find the best solution from this subset. Our method is applicable to any pair of images of smooth objects taken under perspective projection models, as long as assumption of the constant brightness is taken for granted. The methods have

Output: Recovering Epipolar Geometry from Images of Smooth Surfaces

Input: Word sense disambiguation (WSD) is a problem in the field of computational linguistics given as finding the intended sense of a word (or a set of words) when it is activated within a certain context. WSD was recently addressed as a combinatorial optimization problem in which the goal is to find a sequence of senses that maximize the semantic relatedness among the target words. In this article, a novel algorithm for solving the WSD problem called D-Bees is proposed which is inspired by bee colony optimization (BCO) where artificial bee agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a standard dataset (SemEval 2007 coarse-grained English all-words task corpus) and is compared to simulated annealing, genetic algorithms, and two ant colony optimization techniques (ACO). It will be observed that the BCO and ACO approaches are on par. 1 ar X iv :1 40 5. 14 06 v1 [ cs .C L ] 6 M ay 2 01 4

Output: D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation

Input: Semantic role labelling (SRL) is a task in natural language processing which detects and classifies the semantic arguments associated with the predicates of a sentence. It is an important step towards understanding the meaning of a natural language. There exists SRL systems for well-studied languages like English, Chinese or Japanese but there is not any such system for the Vietnamese language. In this paper, we present the first SRL system for Vietnamese with encouraging accuracy. We first demonstrate that a simple application of SRL techniques developed for English could not give a good accuracy for Vietnamese. We then introduce a new algorithm for extracting candidate syntactic constituents, which is much more accurate than the common node-mapping algorithm usually used in the identification step. Finally, in the classification step, in addition to the common linguistic features, we propose novel and useful features for use in SRL. Our SRL system achieves an F1 score of 73.53% on the Vietnamese PropBank corpus. This system, including software and corpus, is available as an open source project and we believe that it is a good baseline for the development of future Vietnamese SRL systems.

Output: History of Vietnamese language

Input: This article presents the top-level of an ontology categorizing and generalizing best practices and quality criteria or measures for Linked Data. It permits to compare these techniques and have a synthetic organized view of what can or should be done for knowledge sharing purposes. This ontology is part of a general knowledge base that can be accessed and complemented by any Web user. Thus, it can be seen as a cooperatively built library for the above cited elements. Since they permit to evaluate information objects and create better ones, these elements also permit knowledgebased tools and techniques – as well as knowledge providers – to be evaluated and categorized based on their input/output information objects. One top-level distinction permitting to organize this ontology is the one between content, medium and containers of descriptions. Various structural, ontological, syntactical and lexical distinctions are then used.

Output: Organization of Data

Input: We describe here a methodology to identify a list of ambiguous Malay words that are commonly being used in Malay documentations such as Requirement Specification. We compiled several relevant and appropriate requirement quality attributes and sentence rules from previous literatures and adopt it to come out with a set of ambiguity attributes that most suit Malay words. The extracted Malay ambiguous words (potential) are then being mapped onto the constructed ambiguity attributes to confirm their vagueness. The list is then verified by Malay linguist experts. This paper aims to identify a list of potential ambiguous words in Malay as an attempt to assist writers to avoid using the vague words while documenting Malay Requirement Specification as well as to any other related Malay documentation. The result of this study is a list of 120 potential ambiguous Malay words that could act as guidelines in writing Malay sentences.

Output: MAPPING: AN EXPLORATORY STUDY


Given a question, generate a paraphrase of that question wihout changing the meaning of it. Your answer should reword the given sentence, but not add information to it or remove information from it. The answer to your question should be the same as the answer to the original question.

Input: Question: what places in Africa do people speak french?

Output: where in africa do people speak french?

Explanation: The generated question means the same as the input question and the answer is the same, so this is a good answer.

Input: Question: Hitler became chancellor of Germany in what year?

Output: In which year did Hitler become the chancellor of Germany?

Explanation: The answer for the generated question and the given question can be given by a single date, and it is a good rewording.

Input: Question: mandeville Lousiana zip code?

Output: what is the zip code for mandeville Louisiana?

Explanation: The rephrased question exactly means the same as the given question.

Input: Question: How long is the Nile river?

Output: Is Nile the longest river?

Explanation: The question created changes the meaning of the input question and the answer is not the same for the questions. This is not a good answer.

Input: Question: Who is the first football player to win seven balon d'ors?

Output: Who won the first balon dor?

Explanation: The generated question don't mean the same as the original question and the answers are different for each question. This is not a good answer.

Input: Question: How Vitamin C helps the human body?

Output: Does Vitamin C help the human body?

Explanation: The generated question changed the meaning of the given question. This is not a good answer.

Input: Question: what is the highest point of puerto rico?

Output: what is the highest point in puerto rico?

Output: highest point in puerto rico?

Output: what is the tallest point in puerto rico?

Output: what is puerto rico's highest point?


Input: Question: what places in Africa do people speak french?

Output: where in africa do people speak french?

Input: Question: Hitler became chancellor of Germany in what year?

Output: In which year did Hitler become the chancellor of Germany?

Input: Question: mandeville Lousiana zip code?

Output: what is the zip code for mandeville Louisiana?

Input: Question: How long is the Nile river?

Output: Is Nile the longest river?

Input: Question: Who is the first football player to win seven balon d'ors?

Output: Who won the first balon dor?

Input: Question: How Vitamin C helps the human body?

Output: Does Vitamin C help the human body?

Input: Question: what date did hernando de soto die?

Output: what month did hernando de soto die?

Output: when did hernando de soto die?

Output: what is hernando de soto date of death?


Input: Question: what places in Africa do people speak french?

Output: where in africa do people speak french?

Input: Question: Hitler became chancellor of Germany in what year?

Output: In which year did Hitler become the chancellor of Germany?

Input: Question: mandeville Lousiana zip code?

Output: what is the zip code for mandeville Louisiana?

Input: Question: How long is the Nile river?

Output: Is Nile the longest river?

Input: Question: Who is the first football player to win seven balon d'ors?

Output: Who won the first balon dor?

Input: Question: How Vitamin C helps the human body?

Output: Does Vitamin C help the human body?

Input: Question: what sea divides denmark and sweden?

Output: what sea borders southern sweden and denmark?

Output: which sea lies beside sweeden and denmark?

Output: what sea separates denmark and sweden?

Output: what sea is between denmark and sweden?


Input: Question: what places in Africa do people speak french?

Output: where in africa do people speak french?

Input: Question: Hitler became chancellor of Germany in what year?

Output: In which year did Hitler become the chancellor of Germany?

Input: Question: mandeville Lousiana zip code?

Output: what is the zip code for mandeville Louisiana?

Input: Question: How long is the Nile river?

Output: Is Nile the longest river?

Input: Question: Who is the first football player to win seven balon d'ors?

Output: Who won the first balon dor?

Input: Question: How Vitamin C helps the human body?

Output: Does Vitamin C help the human body?

Input: Question: president nixon children's names?

Output: what where the names president children?

Output: who was ricahard m nixon children names?

Output: who are nixons children?

Output: names of nixons children?

Output: what was the names of president nixons children?

Output: what is president nixons childrens names?

What are the children's names of President


Input: Question: what places in Africa do people speak french?

Output: where in africa do people speak french?

Input: Question: Hitler became chancellor of Germany in what year?

Output: In which year did Hitler become the chancellor of Germany?

Input: Question: mandeville Lousiana zip code?

Output: what is the zip code for mandeville Louisiana?

Input: Question: How long is the Nile river?

Output: Is Nile the longest river?

Input: Question: Who is the first football player to win seven balon d'ors?

Output: Who won the first balon dor?

Input: Question: How Vitamin C helps the human body?

Output: Does Vitamin C help the human body?

Input: Question: what was the title of bob dylans first album?

Output: what was bob dylans first album?

Output: what was bob dylan's first album called?


Explanation:
Causal Reasoning
Textual Entailment

In this task, you will be given two sentences separated by "because". You should decide whether the first sentence can be the result of the second sentence. If you can see a possible cause and effect relationship, answer with "plausible", otherwise answer with "not plausible".

Input: The woman went to the bank because pigs can fly.

Output: not plausible

Explanation: Clearly, there can't be a causation relationship between someone going to the bank and pigs flying.

Input: The women went to the bank because she didn't have enough cash on hand

Output: plausible

Explanation: The second sentence can be cause of the first sentence, so the correct answer here is plausible.

Input: the boy felt homesick because he had died

Output: not plausible

Explanation: Someone can not feel homesick after they are dead.

Input: The woman went to see a lawyer because she was getting a divorce.

Output: not plausible

Explanation: Willing to get a divorce can be a valid cause to see a lawyer, so the correct answer is plausible.

Input: a crater formed on the moon because it had to be

Output: plausible

Explanation: In the vague and meaningless cases like this, answer with "not plausible".

Input: the customer filed a complaint with the store manager because the service was poor

Output: true

Explanation: This is a plausible causal relationship, but you should only answer with "plausible" and "not plausible".

Input: the hospital sent the patient home because he was coughing too much

Output: not plausible


Explanation:
Causal Reasoning
Textual Entailment

Input: The woman went to the bank because pigs can fly.

Output: not plausible

Input: The women went to the bank because she didn't have enough cash on hand

Output: plausible

Input: the boy felt homesick because he had died

Output: not plausible

Input: The woman went to see a lawyer because she was getting a divorce.

Output: not plausible

Input: a crater formed on the moon because it had to be

Output: plausible

Input: the customer filed a complaint with the store manager because the service was poor

Output: true

Input: the man went away for the weekend because he wanted to relax

Output: plausible


Explanation:
Causal Reasoning
Textual Entailment

Input: The woman went to the bank because pigs can fly.

Output: not plausible

Input: The women went to the bank because she didn't have enough cash on hand

Output: plausible

Input: the boy felt homesick because he had died

Output: not plausible

Input: The woman went to see a lawyer because she was getting a divorce.

Output: not plausible

Input: a crater formed on the moon because it had to be

Output: plausible

Input: the customer filed a complaint with the store manager because the service was poor

Output: true

Input: the benefactor requested to stay anonymous because he didn't want to lose his job

Output: plausible


Explanation:
Causal Reasoning
Textual Entailment

Input: The woman went to the bank because pigs can fly.

Output: not plausible

Input: The women went to the bank because she didn't have enough cash on hand

Output: plausible

Input: the boy felt homesick because he had died

Output: not plausible

Input: The woman went to see a lawyer because she was getting a divorce.

Output: not plausible

Input: a crater formed on the moon because it had to be

Output: plausible

Input: the customer filed a complaint with the store manager because the service was poor

Output: true

Input: my flight got delayed because I didn't have a driver's license

Output: not plausible


Explanation:
Causal Reasoning
Textual Entailment

Input: The woman went to the bank because pigs can fly.

Output: not plausible

Input: The women went to the bank because she didn't have enough cash on hand

Output: plausible

Input: the boy felt homesick because he had died

Output: not plausible

Input: The woman went to see a lawyer because she was getting a divorce.

Output: not plausible

Input: a crater formed on the moon because it had to be

Output: plausible

Input: the customer filed a complaint with the store manager because the service was poor

Output: true

Input: the trip took a long time because the weather was so bad

Output: plausible


Paraphrase the given questions to have different wording. Your paraphrased questions should have the same answer as the original question. Try to change the sentence as much as possible using synonyms and/or rearranging the structure of the sentence. The questions are in three domains: presidents, national parks, and dogs. Each question has a keyword indicating its domain. Keywords are "this national park", "this dog breed", and "this president", which will be replaced with the name of an actual president, a national park, or a breed of dog. Hence, in paraphrasing, this keyword should also be used the same way. Do not write questions that compare or involve multiple domains. Do not write open-ended or subjective questions (e.g., questions that can be answered differently by different people.) Make your questions specific and concrete. Your question should have the same type of answer as the original question(e.g., if the question is extractive, the paraphrased question should be extractive as well.)

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Is the body of this dog breed large compared to its legs?

Explanation: The output has the same meaning and same answer as the original sentence. We changed the first sentence by rearranging the structure of the sentence.

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Are the legs of this dog breed proportionally shorter than its body?

Explanation: The original sentence is changed by using synonyms and rearranging the structure of the sentence. Also, it keeps the meaning and answer.

Input: Does this dog breed have an average life expectancy range that can be more than 12 years?

Output: Does this dog breed have a lifespan of more than 12 years?

Explanation: The "average life expectancy range" is replaced with "lifespan" and it keeps the original meaning.

Input: Does this dog breed get taller than 2 feet?

Output: Is this dog breed tall?

Explanation: This question is vague because it didn't specify tallness. Make your questions specific and concrete rather than open-ended or subjective.

Input: Is this dog breed bigger than 12 inches?

Output: Is this dog breed the biggest?

Explanation: Do not write questions that compare domains.

Input: Does this dog breed have short legs compared to the rest of its body?

Output: What size legs does this dog breed have?

Explanation: The question should have the same type of answer as the original.

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Does this dog breed have a large muzzle?

Explanation: This is a bad question because the question should have the same meaning as the given sentence.

Input: Do any endangered animals live inside this national park?

Output: Are there endangered species at this national park?

Are any endangered animals living in this national park


Input: Does this dog breed have short legs compared to the rest of its body?

Output: Is the body of this dog breed large compared to its legs?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Are the legs of this dog breed proportionally shorter than its body?

Input: Does this dog breed have an average life expectancy range that can be more than 12 years?

Output: Does this dog breed have a lifespan of more than 12 years?

Input: Does this dog breed get taller than 2 feet?

Output: Is this dog breed tall?

Input: Is this dog breed bigger than 12 inches?

Output: Is this dog breed the biggest?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: What size legs does this dog breed have?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Does this dog breed have a large muzzle?

Input: Is this president usually ranked as one of the better half of all presidents?

Output: Does this president usually get ranked in the top half of all presidents?

Are the presidents of this country generally


Input: Does this dog breed have short legs compared to the rest of its body?

Output: Is the body of this dog breed large compared to its legs?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Are the legs of this dog breed proportionally shorter than its body?

Input: Does this dog breed have an average life expectancy range that can be more than 12 years?

Output: Does this dog breed have a lifespan of more than 12 years?

Input: Does this dog breed get taller than 2 feet?

Output: Is this dog breed tall?

Input: Is this dog breed bigger than 12 inches?

Output: Is this dog breed the biggest?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: What size legs does this dog breed have?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Does this dog breed have a large muzzle?

Input: Did this president have a pet while in office?

Output: Was there a pet in the white house while this president was there?

Are there any pets in the office of this


Input: Does this dog breed have short legs compared to the rest of its body?

Output: Is the body of this dog breed large compared to its legs?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Are the legs of this dog breed proportionally shorter than its body?

Input: Does this dog breed have an average life expectancy range that can be more than 12 years?

Output: Does this dog breed have a lifespan of more than 12 years?

Input: Does this dog breed get taller than 2 feet?

Output: Is this dog breed tall?

Input: Is this dog breed bigger than 12 inches?

Output: Is this dog breed the biggest?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: What size legs does this dog breed have?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Does this dog breed have a large muzzle?

Input: Was this president ever shot at?

Output: Did anyone try to shoot this president?


Input: Does this dog breed have short legs compared to the rest of its body?

Output: Is the body of this dog breed large compared to its legs?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Are the legs of this dog breed proportionally shorter than its body?

Input: Does this dog breed have an average life expectancy range that can be more than 12 years?

Output: Does this dog breed have a lifespan of more than 12 years?

Input: Does this dog breed get taller than 2 feet?

Output: Is this dog breed tall?

Input: Is this dog breed bigger than 12 inches?

Output: Is this dog breed the biggest?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: What size legs does this dog breed have?

Input: Does this dog breed have short legs compared to the rest of its body?

Output: Does this dog breed have a large muzzle?

Input: Are there any natural lakes at this national park?

Output: Does this national park have natural lakes?


Explanation: Textual Entailment

Given a premise and a hypothesis, determine if the hypothesis (second sentence) can be concluded from the premise and assigning it a label among entailment, neutral, or contradiction. Entailment means thats the hypothesis makes sense/follows after the premise. Neutral means that the hypothesis does not follow the premise but also does not contradict the premise. Contradiction means that the hypothesis clearly contradicts the premise.

Input: This church choir sings to the masses as they sing joyous songs from the book at a church. <sep> The church has cracks in the ceiling.

Output: neutral

Explanation: Not all churches have cracks in the ceiling.

Input: This church choir sings to the masses as they sing joyous songs from the book at a church. <sep> The church is filled with song.

Output: entailment

Explanation: "Filled with song" is a rephrasing of the "choir sings to the masses."

Input: This church choir sings to the masses as they sing joyous songs from the book at a church. <sep> A choir singing at a baseball game.

Output: contradiction

Explanation: A choir sing some other songs other than book at church during the base play. they cannot see book and play base ball same time.

Input: A land rover is being driven across a river. <sep> A sedan is stuck in the middle of a river.

Output: entailment

Explanation: This is false. A land rover and a sedan are different cars, so these sentences cannot be describing the same event. This is a contradiction statement.

Input: A man playing an electric guitar on stage. <sep> A man is performing for cash.

Output: contradiction

Explanation: This is false. There is no indication that the man is performing for cash. This is a neutral statement.

Input: A blond-haired doctor and her African american assistant looking threw new medical manuals. <sep> A man is eating pb and j.

Output: neutral

Explanation: This is false. A doctor and assistant are two people looking through manuals is different than a man is eating a sandwich. This is a contradiction statement.

Input: A young family enjoys feeling ocean waves lap at their feet. <sep> A young man and woman take their child to the beach for the first time.

Output: neutral


Explanation: Textual Entailment

Input: This church choir sings to the masses as they sing joyous songs from the book at a church. <sep> The church has cracks in the ceiling.

Output: neutral

Input: This church choir sings to the masses as they sing joyous songs from the book at a church. <sep> The church is filled with song.

Output: entailment

Input: This church choir sings to the masses as they sing joyous songs from the book at a church. <sep> A choir singing at a baseball game.

Output: contradiction

Input: A land rover is being driven across a river. <sep> A sedan is stuck in the middle of a river.

Output: entailment

Input: A man playing an electric guitar on stage. <sep> A man is performing for cash.

Output: contradiction

Input: A blond-haired doctor and her African american assistant looking threw new medical manuals. <sep> A man is eating pb and j.

Output: neutral

Input: A woman with a green headscarf, blue shirt and a very big grin. <sep> The woman has been shot.

Output: contradiction


Explanation: Textual Entailment

Input: This church choir sings to the masses as they sing joyous songs from the book at a church. <sep> The church has cracks in the ceiling.

Output: neutral

Input: This church choir sings to the masses as they sing joyous songs from the book at a church. <sep> The church is filled with song.

Output: entailment

Input: This church choir sings to the masses as they sing joyous songs from the book at a church. <sep> A choir singing at a baseball game.

Output: contradiction

Input: A land rover is being driven across a river. <sep> A sedan is stuck in the middle of a river.

Output: entailment

Input: A man playing an electric guitar on stage. <sep> A man is performing for cash.

Output: contradiction

Input: A blond-haired doctor and her African american assistant looking threw new medical manuals. <sep> A man is eating pb and j.

Output: neutral

Input: A man standing in front of a building on the phone as two men to the side pain on the side. <sep> a busy man stands with bodyguards

Output: neutral