inputs stringlengths 234 802 | targets stringclasses 18
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Given the task definition and input, reply with output. Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Republika Srpska secession from Bosnia and Herzegovina
Argument: Republika Srpska secess... | against | 5 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Problem:Topic: Needle exchanges
Argument: Needle exchanges decrease infections and therefore costs.
Solution: | in favor | 8 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Banning vuvuzela horns at the 2010 World Cup
Argument: Vuvuzelas can be used as weapons.
A: | in favor | 4 | NIv2 | task209_stancedetection_classification | zs_opt |
Teacher:Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Teacher: Now, understand the problem? Solve this instance: Topic: 2009 US economic stimulus
Argument: Stimulus increases debt, inflation, intere... | against | 6 | NIv2 | task209_stancedetection_classification | zs_opt |
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: Crime cameras
Argument: Cameras help protect citizens' liberties against crime.
Output: | in favor | 2 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: NATO expansion
Argument: Croatia is doing well at securing stability.
A: | in favor | 4 | NIv2 | task209_stancedetection_classification | zs_opt |
You will be given a definition of a task first, then some input of the task.
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Death penalty
Argument: The death penalty is often motivated by dis... | against | 1 | NIv2 | task209_stancedetection_classification | zs_opt |
Q: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: High-speed rail
Argument: High-speed rail frees up existing rail for other purposes.
A: | in favor | 7 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Superdelegates
Argument: Superdelegates represent old era of closed-door politics.
A: | against | 9 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the task definition and input, reply with output. Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Legalization of adult incest
Argument: Homosexuality is more benign than incest on the f... | against | 5 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Legalization of adult incest
Argument: Banning adult incest is analogous to banning homosexuality.
A: | in favor | 4 | NIv2 | task209_stancedetection_classification | zs_opt |
Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: No Child Left Behind Act
Argument: No Child Left Behind offers valuable measure of student progress.
Output: | in favor | 3 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Problem:Topic: Ending US sanctions on Cuba
Argument: Sanctions against Cuba violated laws on the freedom of navigation.
Solution: | in favor | 8 | NIv2 | task209_stancedetection_classification | zs_opt |
Q: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Constitutionality of US health insurance mandates
Argument: Mandates create cartel of govt-supported insurance companies.
A: | against | 7 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Home plate collision rule in baseball
Argument: Home base collisions are essential characteristic of hardball.
A: | in favor | 4 | NIv2 | task209_stancedetection_classification | zs_opt |
Teacher:Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Teacher: Now, understand the problem? Solve this instance: Topic: Free trade
Argument: Global governance will make governing free trade possible... | in favor | 6 | NIv2 | task209_stancedetection_classification | zs_opt |
Q: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Colonization of the Moon
Argument: Growing crops on the moon faces many difficult challenges.
A: | against | 7 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Problem:Topic: More troops to Afghanistan under Obama
Argument: Counter-terrorism w/o troops did not work in 1990s.
Solution: | in favor | 8 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Fairness Doctrine
Argument: Fairness Doctrine would counter conservative domination of radio.
A: | in favor | 4 | NIv2 | task209_stancedetection_classification | zs_opt |
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: Legalization of drugs
Argument: Individuals have the right to control their bodies and consume drugs.
Output: | in favor | 2 | NIv2 | task209_stancedetection_classification | zs_opt |
Teacher:Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Teacher: Now, understand the problem? Solve this instance: Topic: Prisoners right to vote
Argument: Criminals, dangerous to society, are dangero... | against | 6 | NIv2 | task209_stancedetection_classification | zs_opt |
Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: Natural gas vehicles
Argument: Natural gas utilities have a long record of safety.
Output: | in favor | 3 | NIv2 | task209_stancedetection_classification | zs_opt |
Teacher:Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Teacher: Now, understand the problem? Solve this instance: Topic: Gay adoption
Argument: Children do better with mother and father role models.
... | against | 6 | NIv2 | task209_stancedetection_classification | zs_opt |
You will be given a definition of a task first, then some input of the task.
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Open primaries
Argument: Open primaries produce competitive, substa... | in favor | 1 | NIv2 | task209_stancedetection_classification | zs_opt |
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: NATO expansion
Argument: NATO expansion threatens and antagonizes Russia.
Output: | against | 2 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the task definition and input, reply with output. Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Wave power
Argument: Wave energy more about profits than environment.
| against | 5 | NIv2 | task209_stancedetection_classification | zs_opt |
Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: Two-state solution to Israeli-Palestinian conflict
Argument: Inclusive one-state solution adopts democratic principles.
Outp... | against | 3 | NIv2 | task209_stancedetection_classification | zs_opt |
You will be given a definition of a task first, then some input of the task.
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Child beauty pageants
Argument: Pageants based on bad idea of child... | against | 1 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Problem:Topic: Solar energy
Argument: Nuclear power is a more potent and efficient source of energy.
Solution: | against | 8 | NIv2 | task209_stancedetection_classification | zs_opt |
You will be given a definition of a task first, then some input of the task.
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Big government
Argument: Big government fosters harmful entitlement... | against | 1 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the task definition and input, reply with output. Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: EU elected president
Argument: Electing an EU president directly will increase accountab... | in favor | 5 | NIv2 | task209_stancedetection_classification | zs_opt |
You will be given a definition of a task first, then some input of the task.
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Waterboarding
Argument: Waterboarding is a mild interrogation techn... | in favor | 1 | NIv2 | task209_stancedetection_classification | zs_opt |
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: Seattle deep-bore tunnel
Argument: Tunnel will increase traffic in pioneer square.
Output: | against | 2 | NIv2 | task209_stancedetection_classification | zs_opt |
You will be given a definition of a task first, then some input of the task.
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Geoengineering
Argument: Many geoengineering approaches are harmles... | in favor | 1 | NIv2 | task209_stancedetection_classification | zs_opt |
Teacher:Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Teacher: Now, understand the problem? Solve this instance: Topic: Hydrogen vehicles
Argument: Hydrogen can't be compressed to give sufficient dr... | against | 6 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Oil sands
Argument: Tar sands unnecessary if focus is placed on clean energy. | against | 0 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Waterboarding
Argument: Torture is justified in saving lives.
A: | in favor | 9 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Tibet independence
Argument: The right to self-determination must be offered sparingly to avoid international instabili... | against | 9 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Ban on laser pointers
Argument: Business people use lasers to give presentations. | against | 0 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Problem:Topic: Wave power
Argument: Focus on energy conservation before wave power.
Solution: | against | 8 | NIv2 | task209_stancedetection_classification | zs_opt |
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: Breastfeeding in public
Argument: That breastfeeding is natural does not make it decent in public.
Output: | against | 2 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Mandatory calorie counts on menus
Argument: Calorie counts will be ignored by some, but help others. | in favor | 0 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the task definition and input, reply with output. Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Osama Bin Laden Sea Burial
Argument: Why give Osama Bin Laden a dignified religious buri... | against | 5 | NIv2 | task209_stancedetection_classification | zs_opt |
Teacher:Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Teacher: Now, understand the problem? Solve this instance: Topic: Assault weapons ban in the United States
Argument: Statistics gathered during ... | neutral | 6 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Underground nuclear waste storage
Argument: Underground storage important to safety across borders. | in favor | 0 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Problem:Topic: Castration of sex offenders
Argument: Innocent shouldn't face choice of more prison vs. castration.
Solution: | against | 8 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Balanced budget amendment to US Constitution
Argument: Congress shouldn't make Amend if it can't make good fiscal leg.
A: | against | 4 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Republika Srpska secession from Bosnia and Herzegovina
Argument: Russian backing of Republika Srpska ind. could spark global conflict. | against | 0 | NIv2 | task209_stancedetection_classification | zs_opt |
You will be given a definition of a task first, then some input of the task.
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: DREAM Act
Argument: University leaders almost universally support D... | in favor | 1 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Problem:Topic: Direct democracy
Argument: Direct democracy lacks accountability of decision-making.
Solution: | against | 8 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the task definition and input, reply with output. Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: EU elected president
Argument: Being a part of the process is an important requirement i... | in favor | 5 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Geoengineering, solar shading
Argument: Volcanoes perform solar shading; why is it wrong for humans to do so?.
A: | in favor | 4 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Big government
Argument: Govt bureaucrats pursue more power to advance careers.
A: | against | 9 | NIv2 | task209_stancedetection_classification | zs_opt |
Teacher:Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Teacher: Now, understand the problem? Solve this instance: Topic: 2009 US economic stimulus
Argument: New Deal did little-to-nothing to end the ... | against | 6 | NIv2 | task209_stancedetection_classification | zs_opt |
Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: Banning Muslim hijab
Argument: Muslim head-coverings help keep women safe.
Output: | against | 3 | NIv2 | task209_stancedetection_classification | zs_opt |
Q: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Cellulosic ethanol
Argument: Processing cellulose is more difficult than starches.
A: | against | 7 | NIv2 | task209_stancedetection_classification | zs_opt |
Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: Libertarianism
Argument: Libertarianism does not allow for essential public services (i.e. roads).
Output: | against | 3 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Boycott of 2008 Olympics in China
Argument: China's unwillingness influence Burma's crisis justifies an Olympic Boycott. | in favor | 0 | NIv2 | task209_stancedetection_classification | zs_opt |
Teacher:Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Teacher: Now, understand the problem? Solve this instance: Topic: Was the War in Iraq worth it?
Argument: Iraq's democratic government is workin... | in favor | 6 | NIv2 | task209_stancedetection_classification | zs_opt |
Definition: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Input: Topic: Banning Muslim hijab
Argument: Headscarves falsely presume that men can't contain sexual impulses.
Output: | in favor | 2 | NIv2 | task209_stancedetection_classification | zs_opt |
Detailed Instructions: Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Prostitution
Argument: Common prostitution is not a biblical conflict.
A: | in favor | 9 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Q: Topic: Filibuster
Argument: Filibuster helps flag controversial issues for the public.
A: | in favor | 4 | NIv2 | task209_stancedetection_classification | zs_opt |
Teacher:Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Teacher: Now, understand the problem? Solve this instance: Topic: Gay marriage
Argument: Gay marriage undercuts procreation at time of vulnerabi... | against | 6 | NIv2 | task209_stancedetection_classification | zs_opt |
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Merit pay for teachers
Argument: Merit pay punishes teachers assigned to bad students. | against | 0 | NIv2 | task209_stancedetection_classification | zs_opt |
You will be given a definition of a task first, then some input of the task.
Given the Target and Argument texts detect the stance that the argument has towards the topic. There are three types of stances "in favor", "against", and "neutral".
Topic: Nuclear energy
Argument: Uranium is abundant and will last for hund... | in favor | 1 | NIv2 | task209_stancedetection_classification | zs_opt |
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