inputs stringlengths 234 802 | targets stringclasses 18
values | _template_idx int64 0 9 | _task_source stringclasses 1
value | _task_name stringclasses 1
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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 ... | 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 a... | 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
Argume... | 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... | 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, argu... | 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 Illega... | 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: agains... | 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, a... | 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
questi... | 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: Consid... | 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 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".
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... | 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, a... | 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 cap... | 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, argu... | 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-19... | 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 ... | 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... | 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... | 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 sta... | 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, argu... | 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 assau... | 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... | 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 ... | 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
Argum... | 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
PROBL... | 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: Thre... | 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, a... | 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 ... | 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]: T... | 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... | 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 ... | 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
Argu... | 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: agains... | 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
Argume... | 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: ... | 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: agains... | 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 favo... | 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, argu... | 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:
Top... | 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 C... | 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: Geoenginee... | 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: agains... | 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... | 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, a... | 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... | 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 ... | 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... | 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 ... | 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 agains... | 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
PROBLE... | 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, argu... | 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 ... | 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... | 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 In... | 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... | 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: Co... | 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 Naga... | 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 ... | 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: agains... | 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 ... | 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: To... | 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, a... | 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
Argume... | 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... | 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
Argume... | 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
Arg... | 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: Top... | 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 Doc... | 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 agains... | 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 agains... | 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 ... | 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... | 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 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".
One example: Topic: Three Gorges Dam
Argument: The Three Gorges Dam is a cause of corruption.
Solution is here: against
Explanation: Here, argument is 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".
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 agains... | 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 agains... | 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 abun... | 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 Inpu... | 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 bio... | 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
Argume... | 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 ... | 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, a... | 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 st... | 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... | 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, a... | 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: Ca... | 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, a... | 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... | 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 NA... | 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 favo... | 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
Argu... | 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... | 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 ... | 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: agains... | 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: C... | 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 Inpu... | 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 ... | 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 ... | 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
... | Output: in favor
| 2 | NIv2 | task209_stancedetection_classification | fs_opt |
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