inputs stringlengths 234 802 | targets stringclasses 18 values | _template_idx int64 0 9 | _task_source stringclasses 1 value | _task_name stringclasses 1 value | _template_type stringclasses 2 values |
|---|---|---|---|---|---|
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: Falkland Islands, return of
Argument: A few notable dates :.
A: neutral
****
Q: Topic: Free trade
Argument: Free trade promotes peace and stability internationally.
A: in favor
****
Q: Topic: Affirmative action
Argument: Affirm action justly asks whites to sacrifice for common good.
A: | in favor
****
| 4 | NIv2 | task209_stancedetection_classification | fs_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".
One example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution is here: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Now, solve this: Topic: Catholic Church contraception policy
Argument: Natural methods of contraception can provide the solution to high population growth.
Solution: | in favor | 6 | NIv2 | task209_stancedetection_classification | fs_opt |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance 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: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Why? Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
New input: Topic: Hydroelectric dams
Argument: Dams eliminate forests that absorb CO2 from the atmosphere.
Solution: | against | 0 | NIv2 | task209_stancedetection_classification | fs_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: Puerto Rico statehood in America
Argument: Puerto Rico would burden US welfare system.
[A]: against
[Q]: Topic: Hybrid vehicles
Argument: Hybrids increase efficiency by shutting engines down while idling.
[A]: in favor
[Q]: Topic: Palestinian right of return
Argument: Denationalization of Palestinians illegal; have right of return.
[A]: | in favor
| 5 | NIv2 | task209_stancedetection_classification | fs_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".
Example input: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Example output: against
Example explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: South Ossetia independence
Argument: The world needs fewer borders; A S. Ossetian nation adds more.
A: | against | 3 | NIv2 | task209_stancedetection_classification | fs_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: Big government
Argument: European-style big governments foster more stable societies.
A: in favor
****
Q: Topic: Drivers licenses for Illegal immigrants in the US
Argument: DMV employees would not need to become immigration experts to determine the illegality of an immigrant.
A: in favor
****
Q: Topic: South Ossetia independence
Argument: The world needs fewer borders; A S. Ossetian nation adds more.
A: | against
****
| 4 | NIv2 | task209_stancedetection_classification | fs_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".
See one example below:
Problem: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Problem: Topic: Pornography
Argument: Pornography is an expression falling under freedom of speech.
Solution: | in favor | 4 | NIv2 | task209_stancedetection_classification | fs_opt |
Part 1. 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".
Part 2. Example
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Answer: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Part 3. Exercise
Topic: EU elected president
Argument: Strong presidential state powers are unnecessary and uncalled for.
Answer: | against | 7 | NIv2 | task209_stancedetection_classification | fs_opt |
instruction:
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".
question:
Topic: Mine Ban Treaty (Ottawa Treaty)
Argument: Deploying smart mines encourages the use of all mines.
answer:
in favor
question:
Topic: High-speed rail
Argument: Unlike automobiles, rail fosters a sense of community.
answer:
in favor
question:
Topic: Google decision to stop censoring results in China
Argument: Google out of China reduces access to free/fair info.
answer:
| against
| 9 | NIv2 | task209_stancedetection_classification | fs_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".
Input: Consider Input: Topic: Withdrawing from Iraq
Argument: An early withdrawal from Iraq would embolden terrorists.
Output: against
Input: Consider Input: Topic: Polygamy
Argument: Polygamy undermines the traditional institution of marriage.
Output: against
Input: Consider Input: Topic: Charter schools
Argument: Charter schools cut through red tape, quickly opening after Katrina.
| Output: in favor
| 2 | NIv2 | task209_stancedetection_classification | fs_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".
One example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution is here: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Now, solve this: Topic: Mine Ban Treaty (Ottawa Treaty)
Argument: Deploying smart mines encourages the use of all mines.
Solution: | in favor | 6 | NIv2 | task209_stancedetection_classification | fs_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".
Ex Input:
Topic: Network neutrality
Argument: Net neutrality may not be good for ISPs, but good overall.
Ex Output:
in favor
Ex Input:
Topic: Infant male circumcision
Argument: Parents have a right to circumcise their children.
Ex Output:
in favor
Ex Input:
Topic: Algae biofuel
Argument: Algae biofuel is biodegradable.
Ex Output:
| in favor
| 1 | NIv2 | task209_stancedetection_classification | fs_opt |
Part 1. 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".
Part 2. Example
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Answer: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Part 3. Exercise
Topic: Single-payer universal health care
Argument: Universal health care lowers long-term health costs.
Answer: | in favor | 7 | NIv2 | task209_stancedetection_classification | fs_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: Trying 9/11 terror suspects in NYC courts
Argument: Little difference between civilian courts and military tribunals.
neutral
Topic: Carbon capture and storage
Argument: Sequestered C02 can be injected into reservoirs to recover oil.
in favor
Topic: Algae biofuel
Argument: Industrial algae biofuel requires too many nutrients.
| against
| 0 | NIv2 | task209_stancedetection_classification | fs_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".
Example input: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Example output: against
Example explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: Three Gorges Dam
Argument: The TGD helps limit the risk of floods in the region.
A: | in favor | 3 | NIv2 | task209_stancedetection_classification | fs_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".
[EX Q]: Topic: Gene patents
Argument: Gene patents help drive major economic breakthroughs.
[EX A]: in favor
[EX Q]: Topic: Return of Israel to pre-1967 borders
Argument: Pre-1967 borders would be too insecure and dangerous.
[EX A]: against
[EX Q]: Topic: High-speed rail
Argument: High-speed trains: costly govt project in search of need.
[EX A]: | against
| 6 | NIv2 | task209_stancedetection_classification | fs_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".
Example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Example solution: against
Example explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Problem: Topic: Employee Free Choice Act
Argument: Employee Free Choice Act protects unionizing workers from intimidation.
| Solution: in favor | 5 | NIv2 | task209_stancedetection_classification | fs_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".
Let me give you an example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
The answer to this example can be: against
Here is why: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
OK. solve this:
Topic: Libertarianism
Argument: Libertarian economics runs contrary to modern economic theory.
Answer: | against | 8 | NIv2 | task209_stancedetection_classification | fs_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".
Ex Input:
Topic: Natural gas vehicles
Argument: Odorless natural gas can escape detection risking fire/explosion.
Ex Output:
against
Ex Input:
Topic: Affirmative action
Argument: Affirm action fills key jobs with less productive individuals.
Ex Output:
against
Ex Input:
Topic: No Child Left Behind Act
Argument: No Child Left Behind lacks non-English tests.
Ex Output:
| against
| 1 | NIv2 | task209_stancedetection_classification | fs_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".
[EX Q]: Topic: Gene patents
Argument: Little evidence exists that gene patents hurt research.
[EX A]: in favor
[EX Q]: Topic: Vehicle fuel economy standards
Argument: Fuel standards impair individuals needing high-powered trucks.
[EX A]: against
[EX Q]: Topic: Mandatory military service
Argument: National service promotes patriotism.
[EX A]: | in favor
| 6 | NIv2 | task209_stancedetection_classification | fs_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".
Example input: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Example output: against
Example explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: 2009 US economic stimulus
Argument: Majority of US stimulus is immediate to fight recession now.
A: | in favor | 3 | NIv2 | task209_stancedetection_classification | fs_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".
[EX Q]: Topic: High-speed rail
Argument: Unlike automobiles, rail fosters a sense of community.
[EX A]: in favor
[EX Q]: Topic: Israeli military assault in Gaza
Argument: Defeating Hamas is key to long-term Israeli/Palestinian solution.
[EX A]: in favor
[EX Q]: Topic: Lowering US drinking age from 21 to 18
Argument: Safer roads with 21 drinking laws outweighs all trade-offs.
[EX A]: | against
| 6 | NIv2 | task209_stancedetection_classification | fs_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: European Monetary Fund
Argument: EMF would allow orderly sovereign default.
in favor
Topic: Israeli settlements
Argument: Jews have historical right to return to West Bank.
in favor
Topic: Solar energy
Argument: Solar electricity cannot significantly reduce dependencies on oil.
| against
| 0 | NIv2 | task209_stancedetection_classification | fs_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? If you are still confused, see the following example:
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Reason: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Now, solve this instance: Topic: Muhammad cartoons controversy
Argument: Because the cartoons were legal, they did not require a government response.
Student: | in favor | 2 | NIv2 | task209_stancedetection_classification | fs_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: Breastfeeding in public
Argument: Breastfeeding can help new mothers lose weight.
A: in favor
****
Q: Topic: UN Security Council veto
Argument: Abolishing veto would enable more global action in the UN.
A: against
****
Q: Topic: Pornography
Argument: Pornography further victimizes sexual abuse victims.
A: | against
****
| 4 | NIv2 | task209_stancedetection_classification | fs_opt |
TASK 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".
PROBLEM: Topic: Abortion
Argument: Legal abortion protects women with serious illnesses that are vulnerable.
SOLUTION: in favor
PROBLEM: Topic: Geoengineering, solar shading
Argument: Sunshield costs are reasonable in face of global warming.
SOLUTION: in favor
PROBLEM: Topic: US debt ceiling deal
Argument: Debt deal makes needed cuts to military budgets.
SOLUTION: | in favor
| 8 | NIv2 | task209_stancedetection_classification | fs_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: Airport security profiling
Argument: Profiling works well for Israel, can work well elsewhere.
in favor
Topic: Three Gorges Dam
Argument: Three Gorges Dam damages water quality.
against
Topic: Free public transportation
Argument: A lot of people would already be using it if it didn't cost so much.
| in favor
| 0 | NIv2 | task209_stancedetection_classification | fs_opt |
Part 1. 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".
Part 2. Example
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Answer: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Part 3. Exercise
Topic: Manned mission to Mars
Argument: Weight of supplies for long Mars trip is impractical.
Answer: | against | 7 | NIv2 | task209_stancedetection_classification | fs_opt |
Given the task definition, example input & output, solve the new input case.
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".
Example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Output: against
Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
New input case for you: Topic: Natural gas vehicles
Argument: Natural gas burns more cleanly than gasoline in general.
Output: | in favor | 1 | NIv2 | task209_stancedetection_classification | fs_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".
[EX Q]: Topic: $700 billion US economic bailout
Argument: $700b plan may result in few losses or actually profit taxpayers.
[EX A]: in favor
[EX Q]: Topic: Vehicle fuel economy standards
Argument: Fuel economy standards increase car manufacturing costs and prices.
[EX A]: against
[EX Q]: Topic: US offshore oil drilling
Argument: Offshore oil exploration could result in a major find.
[EX A]: | in favor
| 6 | NIv2 | task209_stancedetection_classification | fs_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".
Example Input: Topic: Ending US sanctions on Cuba
Argument: Sanctioning Cuba is appropriate punishment for its flouting the UN.
Example Output: against
Example Input: Topic: No Child Left Behind Act
Argument: No Child Left Behind lacks non-English tests.
Example Output: against
Example Input: Topic: Balanced budget amendment to US Constitution
Argument: Balanced budget will bring fed spending in line with states'.
Example Output: | in favor
| 3 | NIv2 | task209_stancedetection_classification | fs_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".
Input: Consider Input: Topic: Hunting for sport
Argument: Animals should be treated as we would want to be treated.
Output: against
Input: Consider Input: Topic: Obama, meeting with hostile foreign leaders without preconditions
Argument: Unconditional meetings wrongly legitimize hostile leaders.
Output: against
Input: Consider Input: Topic: Kyoto Protocol
Argument: Kyoto regulations on C02 emissions do not improve air-quality.
| Output: against
| 2 | NIv2 | task209_stancedetection_classification | fs_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".
--------
Question: Topic: High-speed rail
Argument: High speed rail is a great tourist attraction.
Answer: in favor
Question: Topic: Filibuster
Argument: The term filibuster reflects its historic infamy.
Answer: neutral
Question: Topic: Legalization of drugs
Argument: In the short term it might eliminate drug dealers on our streets but do any of us really think that whatever multinational corporation ends of cornering the drug market will have a problem trading with the existing cartels.
Answer: | against
| 7 | NIv2 | task209_stancedetection_classification | fs_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".
See one example below:
Problem: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Problem: Topic: EU constitution reform treaty (Lisbon Treaty)
Argument: Lisbon strengthens EU diplomatic representation on the global stage.
Solution: | in favor | 4 | NIv2 | task209_stancedetection_classification | fs_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: $700 billion US economic bailout
Argument: $700b plan may result in few losses or actually profit taxpayers.
in favor
Topic: Filibuster
Argument: Filibuster wrongly burdens majority party.
against
Topic: Random sobriety tests for drivers
Argument: RBT has been successfully implemented in many modern democracies.
| in favor
| 0 | NIv2 | task209_stancedetection_classification | fs_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".
--------
Question: Topic: Underground nuclear waste storage
Argument: Underground nuclear waste storage is safest option.
Answer: in favor
Question: Topic: Education vouchers
Argument: Education vouchers improve minority academic achievement.
Answer: in favor
Question: Topic: No Child Left Behind Act
Argument: NCLB has succeeded in improving test scores.
Answer: | in favor
| 7 | NIv2 | task209_stancedetection_classification | fs_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".
See one example below:
Problem: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Problem: Topic: Oil sands
Argument: Tar sands can't enhance energy security; too expensive, not enough.
Solution: | against | 4 | NIv2 | task209_stancedetection_classification | fs_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".
Input: Consider Input: Topic: EU elected president
Argument: Being a part of the process is an important requirement in any democracy.
Output: in favor
Input: Consider Input: Topic: UN Security Council veto
Argument: The UN veto fosters a system of checks and balances.
Output: in favor
Input: Consider Input: Topic: Prostitution
Argument: State resources should not be wasted fighting prostitution.
| Output: in favor
| 2 | NIv2 | task209_stancedetection_classification | fs_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".
Example input: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Example output: against
Example explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: DC handgun ban
Argument: Alternative measures can be taken in place of a ban to stem crime and murder.
A: | against | 3 | NIv2 | task209_stancedetection_classification | fs_opt |
instruction:
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".
question:
Topic: Veal
Argument: Calves are fed inhumane milk diets to keep them anemic and their meat pale.
answer:
against
question:
Topic: Ecotourism
Argument: Ecotourism is good for the human soul and social health.
answer:
in favor
question:
Topic: Osama Bin Laden Sea Burial
Argument: No alternative to sea burial was available.
answer:
| in favor
| 9 | NIv2 | task209_stancedetection_classification | fs_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".
[EX Q]: Topic: Castration of sex offenders
Argument: Sex predators are monsters who forgo most rights.
[EX A]: in favor
[EX Q]: Topic: Employee Free Choice Act
Argument: EFCA strengthens workers' ability and right to unionize.
[EX A]: in favor
[EX Q]: Topic: Legalization of drugs
Argument: The state can tax the sale of legalized drugs and use the revenue for treatment programs.
[EX A]: | in favor
| 6 | NIv2 | task209_stancedetection_classification | fs_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: Health insurance mandates
Argument: Mandatory health insurance is analogous to mandatory car insurance.
A: neutral
****
Q: Topic: Geoengineering, iron fertilization of algae blooms
Argument: Algae bloom solutions require fertilizing too much ocean area.
A: against
****
Q: Topic: Home plate collision rule in baseball
Argument: Pro baseball players are paid to take risks, entertain.
A: | against
****
| 4 | NIv2 | task209_stancedetection_classification | fs_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".
See one example below:
Problem: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Problem: Topic: European missile defense
Argument: A European missile defense system threatens and antagonizes Russia.
Solution: | against | 4 | NIv2 | task209_stancedetection_classification | fs_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".
Let me give you an example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
The answer to this example can be: against
Here is why: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
OK. solve this:
Topic: Mandatory calorie counts on menus
Argument: Calorie counts are ineffective at compelling healthier choices.
Answer: | against | 8 | NIv2 | task209_stancedetection_classification | fs_opt |
Part 1. 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".
Part 2. Example
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Answer: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Part 3. Exercise
Topic: Trying 9/11 terror suspects in NYC courts
Argument: Civilian trials improve global opinion of US, fight on terrorism.
Answer: | in favor | 7 | NIv2 | task209_stancedetection_classification | fs_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".
Let me give you an example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
The answer to this example can be: against
Here is why: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
OK. solve this:
Topic: Banning cell phones in cars
Argument: Careless driving laws are inadequate; cell phone ban is necessary.
Answer: | in favor | 8 | NIv2 | task209_stancedetection_classification | fs_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".
Example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Example solution: against
Example explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Problem: Topic: South Ossetia independence
Argument: Georgia has a right to maintain its internal sovereign integrity.
| Solution: in favor | 5 | NIv2 | task209_stancedetection_classification | fs_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: Turkey EU membership
Argument: A growing EU reduces the significance of Turkey's size and population:.
[A]: against
[Q]: Topic: Mandatory military service
Argument: Mandatory service fosters militarism.
[A]: against
[Q]: Topic: Hydroelectric dams
Argument: The world's rivers are covered with dams; room for expansion is limited.
[A]: | against
| 5 | NIv2 | task209_stancedetection_classification | fs_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? If you are still confused, see the following example:
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Reason: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Now, solve this instance: Topic: Electric vehicles
Argument: Electric vehicles need not be able to quickly recharge.
Student: | neutral | 2 | NIv2 | task209_stancedetection_classification | fs_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".
One example is below.
Q: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
A: against
Rationale: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: Catholic Church contraception policy
Argument: Church contraception policy is supported by natural law:.
A: | in favor | 9 | NIv2 | task209_stancedetection_classification | fs_opt |
TASK 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".
PROBLEM: Topic: New START Treaty
Argument: Signing New START saves US-Russia relations for Iranian problem.
SOLUTION: in favor
PROBLEM: Topic: Turkey EU membership
Argument: A growing EU reduces the significance of Turkey's size and population:.
SOLUTION: against
PROBLEM: Topic: Catholic Church contraception policy
Argument: Church contraception policy is supported by natural law:.
SOLUTION: | in favor
| 8 | NIv2 | task209_stancedetection_classification | fs_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".
Example input: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Example output: against
Example explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: Banning cell phones in cars
Argument: More difficult to enforce hands-free cell phone ban.
A: | neutral | 3 | NIv2 | task209_stancedetection_classification | fs_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".
Example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Example solution: against
Example explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Problem: Topic: Animal testing
Argument: The number of animals used in experiments should be reduced by.
| Solution: against | 5 | NIv2 | task209_stancedetection_classification | fs_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".
--------
Question: Topic: Gene patents
Argument: Gene patents help drive major economic breakthroughs.
Answer: in favor
Question: Topic: $700 billion US economic bailout
Argument: Financial crisis requires and justifies strong executive powers.
Answer: in favor
Question: Topic: Colonization of the Moon
Argument: Moonbase will help answer remaining scientific questions.
Answer: | in favor
| 7 | NIv2 | task209_stancedetection_classification | fs_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".
Example Input: Topic: Hybrid vehicles
Argument: Hybrids increase efficiency by shutting engines down while idling.
Example Output: in favor
Example Input: Topic: Prostitution
Argument: Legalizing prostitution won't substantially reduce HIV/AIDS risks.
Example Output: against
Example Input: Topic: Obama, meeting with hostile foreign leaders without preconditions
Argument: Iranian leaders are evil; wrong to meet with them.
Example Output: | against
| 3 | NIv2 | task209_stancedetection_classification | fs_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".
Let me give you an example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
The answer to this example can be: against
Here is why: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
OK. solve this:
Topic: Needle exchanges
Argument: Needle exchanges decrease infections and therefore costs.
Answer: | in favor | 8 | NIv2 | task209_stancedetection_classification | fs_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".
Input: Consider Input: Topic: Legalization of Marijuana
Argument: Legalization of marijuana will make it more affordable.
Output: in favor
Input: Consider Input: Topic: Employee Free Choice Act
Argument: There is significant public support for EFCA.
Output: in favor
Input: Consider Input: Topic: Ground zero mosque
Argument: Banning ground zero mosque would violate sep of church/state.
| Output: in favor
| 2 | NIv2 | task209_stancedetection_classification | fs_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".
[EX Q]: Topic: Mandatory military service
Argument: Impossible to mandate morality of state.
[EX A]: against
[EX Q]: Topic: Bombing Hiroshima and Nagasaki
Argument: No international law forbade the bombing of Japanese civilians.
[EX A]: in favor
[EX Q]: Topic: Election of judges
Argument: Judicial elections need not be designed as partisan.
[EX A]: | neutral
| 6 | NIv2 | task209_stancedetection_classification | fs_opt |
Given the task definition, example input & output, solve the new input case.
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".
Example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Output: against
Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
New input case for you: Topic: Ground zero mosque
Argument: Banning ground zero mosque would violate sep of church/state.
Output: | in favor | 1 | NIv2 | task209_stancedetection_classification | fs_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".
Example Input: Topic: Medical marijuana dispensaries
Argument: The State is justified in protecting individuals from themselves.
Example Output: against
Example Input: Topic: Graduated response antipiracy laws
Argument: Graduated response helps avoid litigating against consumers.
Example Output: in favor
Example Input: Topic: Health insurance mandates
Argument: Mandate deters uninsured from going to hospital, receiving penalty.
Example Output: | against
| 3 | NIv2 | task209_stancedetection_classification | fs_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".
Example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Example solution: against
Example explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Problem: Topic: Banning vuvuzela horns at the 2010 World Cup
Argument: Vuvuzelas can be used as weapons.
| Solution: in favor | 5 | NIv2 | task209_stancedetection_classification | fs_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".
Example Input: Topic: US debt ceiling deal
Argument: Debt deal doesn't cut spending enough to solve deficit.
Example Output: against
Example Input: Topic: Open primaries
Argument: Primaries too important in democracy to be internal to parties.
Example Output: in favor
Example Input: Topic: Waterboarding
Argument: Torture violates protections of the vulnerable.
Example Output: | against
| 3 | NIv2 | task209_stancedetection_classification | fs_opt |
Part 1. 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".
Part 2. Example
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Answer: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Part 3. Exercise
Topic: 2009 US economic stimulus
Argument: Stimulus increases debt, inflation, interest rates, harms economy.
Answer: | against | 7 | NIv2 | task209_stancedetection_classification | fs_opt |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance 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: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Why? Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
New input: Topic: Puerto Rico statehood in America
Argument: Puerto Rico statehood is not economical for US.
Solution: | against | 0 | NIv2 | task209_stancedetection_classification | fs_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".
Let me give you an example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
The answer to this example can be: against
Here is why: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
OK. solve this:
Topic: Legality of coca production and consumption
Argument: Coca can be used in a variety of products.
Answer: | in favor | 8 | NIv2 | task209_stancedetection_classification | fs_opt |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance 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: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Why? Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
New input: Topic: Merit pay for teachers
Argument: Teachers should be paid more; based on merit.
Solution: | in favor | 0 | NIv2 | task209_stancedetection_classification | fs_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: Kangaroo culling in Australia
Argument: Killing millions of kangaroos does not make it sustainable.
against
Topic: Breastfeeding in public
Argument: Breastfeeding is best for the health and development of babies.
in favor
Topic: Legality of coca production and consumption
Argument: Coca can be used in a variety of products.
| in favor
| 0 | NIv2 | task209_stancedetection_classification | fs_opt |
TASK 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".
PROBLEM: Topic: Electric vehicles
Argument: Electric car power and speed can be precisely controlled.
SOLUTION: in favor
PROBLEM: Topic: Ban on laser pointers
Argument: Business people use lasers to give presentations.
SOLUTION: against
PROBLEM: Topic: Airport security profiling
Argument: Terrorists can easily beat profiling systems.
SOLUTION: | against
| 8 | NIv2 | task209_stancedetection_classification | fs_opt |
instruction:
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".
question:
Topic: Pickens US energy plan
Argument: The Pickens Plan is generally not viable.
answer:
against
question:
Topic: Fairness Doctrine
Argument: Balanced left/right broadcasting exist w/o Fairness Doctrine.
answer:
against
question:
Topic: NATO expansion
Argument: Croatia is doing well at securing stability.
answer:
| in favor
| 9 | NIv2 | task209_stancedetection_classification | fs_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".
One example is below.
Q: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
A: against
Rationale: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: NATO expansion
Argument: Croatia is doing well at securing stability.
A: | in favor | 9 | NIv2 | task209_stancedetection_classification | fs_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".
One example is below.
Q: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
A: against
Rationale: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: Military recruiting in public schools
Argument: Military uses sophisticated persuasion techniques on kids.
A: | against | 9 | NIv2 | task209_stancedetection_classification | fs_opt |
Given the task definition, example input & output, solve the new input case.
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".
Example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Output: against
Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
New input case for you: Topic: Solar energy
Argument: Solar energy can be stored in ways other than batteries (i.e. hydrogen).
Output: | in favor | 1 | NIv2 | task209_stancedetection_classification | fs_opt |
instruction:
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".
question:
Topic: Child beauty pageants
Argument: Plenty of other activities are exclusive to boys/girls.
answer:
in favor
question:
Topic: Legalization of Marijuana
Argument: If marijuana is harmful, isn't this sufficient punishment for users.
answer:
in favor
question:
Topic: Solar energy
Argument: Solar energy can be stored in ways other than batteries (i.e. hydrogen).
answer:
| in favor
| 9 | NIv2 | task209_stancedetection_classification | fs_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".
One example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution is here: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Now, solve this: Topic: High-speed rail
Argument: High-speed rail frees up existing rail for other purposes.
Solution: | in favor | 6 | NIv2 | task209_stancedetection_classification | fs_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".
One example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution is here: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Now, solve this: Topic: US offshore oil drilling
Argument: US offshore drilling would hardly lower global oil prices.
Solution: | against | 6 | NIv2 | task209_stancedetection_classification | fs_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".
One example is below.
Q: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
A: against
Rationale: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: European Monetary Fund
Argument: EMF would allow orderly sovereign default.
A: | in favor | 9 | NIv2 | task209_stancedetection_classification | fs_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".
One example is below.
Q: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
A: against
Rationale: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Q: Topic: Should Hugo Chávez focus on the private sector more than social spending?
Argument: The increase in private sector in the economy causes instability.
A: | against | 9 | NIv2 | task209_stancedetection_classification | fs_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: Puerto Rico statehood in America
Argument: Puerto Rico would burden US welfare system.
against
Topic: Nuclear energy
Argument: Uranium is abundant and will last for hundreds of years.
in favor
Topic: Privatizing social security
Argument: Costly privatization of Soc Sec would dampen econ growth.
| against
| 0 | NIv2 | task209_stancedetection_classification | fs_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".
Example Input: Topic: Medical marijuana dispensaries
Argument: That marijuana is herbal does not mean it is safe.
Example Output: against
Example Input: Topic: Underground nuclear waste storage
Argument: Scientific consensus supports nuclear waste underground storage.
Example Output: in favor
Example Input: Topic: Should Hugo Chávez focus on the private sector more than social spending?
Argument: The increase in private sector in the economy causes instability.
Example Output: | against
| 3 | NIv2 | task209_stancedetection_classification | fs_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 cell phones in cars
Argument: If you can't ban sleep-driving, why ban talking on phone in car.
[A]: against
[Q]: Topic: Algae biofuel
Argument: Algae can be produced locally for food and fuel.
[A]: in favor
[Q]: Topic: Wind energy
Argument: Litigation to clear land (scenery) for windmills can be costly.
[A]: | against
| 5 | NIv2 | task209_stancedetection_classification | fs_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: Direct democracy
Argument: Direct democracy generally reduces the risks of corruption.
A: in favor
****
Q: Topic: Education vouchers
Argument: Vouchers promote innovation and specialisation.
A: in favor
****
Q: Topic: Kosovo independence
Argument: Kosovo's autonomy within Yugoslavia supports moves to independence.
A: | in favor
****
| 4 | NIv2 | task209_stancedetection_classification | fs_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? If you are still confused, see the following example:
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Reason: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Now, solve this instance: Topic: Veal
Argument: Anything which involves murdering animals is cruel .
Student: | against | 2 | NIv2 | task209_stancedetection_classification | fs_opt |
Part 1. 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".
Part 2. Example
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Answer: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Part 3. Exercise
Topic: Joint JD/MBA degree
Argument: Usually one shot at advanced degree; JD/MBA gets both done.
Answer: | in favor | 7 | NIv2 | task209_stancedetection_classification | fs_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".
[EX Q]: Topic: International Criminal Court
Argument: The ICC risks heavy politicization of prosecution.
[EX A]: against
[EX Q]: Topic: Puerto Rico statehood in America
Argument: Puerto Rico would burden US welfare system.
[EX A]: against
[EX Q]: Topic: Libertarianism
Argument: Free market economics fosters capitalist authoritarianism; undermines rights.
[EX A]: | against
| 6 | NIv2 | task209_stancedetection_classification | fs_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: Mine Ban Treaty (Ottawa Treaty)
Argument: Deploying smart mines encourages the use of all mines.
[A]: in favor
[Q]: Topic: Carbon capture and storage
Argument: Sequestered C02 can be injected into reservoirs to recover oil.
[A]: in favor
[Q]: Topic: Hate crime laws
Argument: Hate crime is a major problem, requiring a state response.
[A]: | in favor
| 5 | NIv2 | task209_stancedetection_classification | fs_opt |
Part 1. 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".
Part 2. Example
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Answer: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Part 3. Exercise
Topic: Hate crime laws
Argument: Hate crime is a major problem, requiring a state response.
Answer: | in favor | 7 | NIv2 | task209_stancedetection_classification | fs_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".
[EX Q]: Topic: Carbon capture and storage
Argument: Sequestered C02 can be injected into reservoirs to recover oil.
[EX A]: in favor
[EX Q]: Topic: Carbon emissions trading
Argument: Emissions trading encourages investments in best technologies.
[EX A]: in favor
[EX Q]: Topic: $700 billion US economic bailout
Argument: $700b plan has been corruptly influenced by Wallstreet.
[EX A]: | against
| 6 | NIv2 | task209_stancedetection_classification | fs_opt |
Part 1. 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".
Part 2. Example
Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Answer: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Part 3. Exercise
Topic: No Child Left Behind Act
Argument: No Child Left Behind offers valuable measure of student progress.
Answer: | in favor | 7 | NIv2 | task209_stancedetection_classification | fs_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".
Ex Input:
Topic: Castration of sex offenders
Argument: Castration is about helping, not hating/hurting offender.
Ex Output:
in favor
Ex Input:
Topic: Cellulosic ethanol
Argument: Cellulosic ethanol can generate good-paying jobs.
Ex Output:
in favor
Ex Input:
Topic: Legalization of adult incest
Argument: Sex is for reproduction; incest cannot be only about sex.
Ex Output:
| against
| 1 | NIv2 | task209_stancedetection_classification | fs_opt |
TASK 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".
PROBLEM: Topic: NATO expansion
Argument: The expense of NATO expansion is marginal when compared to the defence budgets of the major NATO States.
SOLUTION: in favor
PROBLEM: Topic: Fish farming ban
Argument: Fish feel pain and should not be made to suffer.
SOLUTION: in favor
PROBLEM: Topic: Polygamy
Argument: Recognizing polygamy would cause a host of legal problems.
SOLUTION: | against
| 8 | NIv2 | task209_stancedetection_classification | fs_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".
--------
Question: Topic: European Union Expansion
Argument: EU enlargement will improve foreign direct investment into eastern Europe.
Answer: in favor
Question: Topic: Deporting illegal immigrants in the US
Argument: Trail of Tears demonstrates injustice of mass deportation.
Answer: against
Question: Topic: MBA
Argument: Getting an MBA later in career and life is fine.
Answer: | in favor
| 7 | NIv2 | task209_stancedetection_classification | fs_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: Banning vuvuzela horns at the 2010 World Cup
Argument: Vuvuzela sales are economically beneficial in S. Africa.
against
Topic: Free trade
Argument: Free trade promotes peace and stability internationally.
in favor
Topic: Primaries in US elections
Argument: Early emphasis on Iowa and New Hampshire disenfranchises minority voters.
| against
| 0 | NIv2 | task209_stancedetection_classification | fs_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: Michigan and Florida delegates in 2008 US elections
Argument: It is unclear who should pay for mail re-votes in Mich and Florida.
A: neutral
****
Q: Topic: Legality of coca production and consumption
Argument: Tradition of coca consumption is a poor argument.
A: against
****
Q: Topic: Child beauty pageants
Argument: Parental ambitions can make kid queens mentally unwell.
A: | against
****
| 4 | NIv2 | task209_stancedetection_classification | fs_opt |
Given the task definition, example input & output, solve the new input case.
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".
Example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Output: against
Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
New input case for you: Topic: Primaries in US elections
Argument: Early emphasis on Iowa and New Hampshire disenfranchises minority voters.
Output: | against | 1 | NIv2 | task209_stancedetection_classification | fs_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".
See one example below:
Problem: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution: against
Explanation: Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
Problem: Topic: Free trade
Argument: Global governance will make governing free trade possible.
Solution: | in favor | 4 | NIv2 | task209_stancedetection_classification | fs_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".
Input: Consider Input: Topic: US offshore oil drilling
Argument: US offshore drilling would hardly lower global oil prices.
Output: against
Input: Consider Input: Topic: Mandatory labeling of genetically modified foods
Argument: Opinion is often divided or ambivalent on labeling GM foods.
Output: neutral
Input: Consider Input: Topic: Free trade
Argument: Global governance will make governing free trade possible.
| Output: in favor
| 2 | NIv2 | task209_stancedetection_classification | fs_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".
Example Input: Topic: Prisoners right to vote
Argument: Criminals, dangerous to society, are dangerous with vote.
Example Output: against
Example Input: Topic: Instant replay in baseball
Argument: Instant replay should not exist for sake of personal achievements.
Example Output: against
Example Input: Topic: US Renewable Electricity Standard
Argument: US Renewable Electricity Standard is popular.
Example Output: | neutral
| 3 | NIv2 | task209_stancedetection_classification | fs_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".
[EX Q]: Topic: Drivers licenses for Illegal immigrants in the US
Argument: DMV employees would not need to become immigration experts to determine the illegality of an immigrant.
[EX A]: in favor
[EX Q]: Topic: Corn ethanol
Argument: Corn ethanol is inferior to sugar ethanol.
[EX A]: against
[EX Q]: Topic: Israeli military assault in Gaza
Argument: Israel's use of white phosphorous in Gaza was a humanitarian crime.
[EX A]: | against
| 6 | NIv2 | task209_stancedetection_classification | fs_opt |
Given the task definition, example input & output, solve the new input case.
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".
Example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Output: against
Here, argument is against the given topic, three gorges dam. Hence, the answer is "against".
New input case for you: Topic: Legalization of drugs
Argument: Individuals have the right to control their bodies and consume drugs.
Output: | in favor | 1 | NIv2 | task209_stancedetection_classification | fs_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".
Input: Consider Input: Topic: Vehicle fuel economy standards
Argument: Fuel standards impair individuals needing high-powered trucks.
Output: against
Input: Consider Input: Topic: Merit pay for teachers
Argument: Merit pay punishes teachers assigned to bad students.
Output: against
Input: Consider 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 | fs_opt |
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