In this task, you're given a fill-in-the-blank question that contains two object names. Additionally, you're given one answer which is one of the objects present in the question. In this task, you need to minimally change the given question so that the answer flips to another object in the question. Remember the question has to be about both objects which are related but different; for example, "trophy" and "suitcase". This task typically involves replacing one word i.e., the 'trigger word' with its antonym (e.g., changing from "small" to "big"). You should not change any content in the given question beyond a word or two, i.e., the trigger word/phrase. The expected answer to your question must not be associated with just the trigger word; instead, it should depend on the context present in the question. The expected answer should not be ambiguous. For your question, there should be an agreed upon answer to fill in the blank. Your generations should NOT contain potentially explicit, offensive, or adult content. In your generated question, retain the object names from the input question. Avoid repeating the same style or phrase in generating your modified question e.g. this task can be always solved using a simple negation i.e. by adding not, never, etc. Instead, try to increase the word diversity. Your question must contain at least 15 and at most 30 words. Your question must have at least 70% overlapping words with the given question. You must utilize the given context word while writing the question. Your question must contain only one blank. The two objects should be used ONCE in your question. Here is a list of attributes and associated contrastive words that may help write contrastive trigger words. Note that contrastive trigger words help in flipping the label.
| Attribute | triggerword | contrastive triggerword | 
| age | old | new | 
| altitude | low | high |
| area | small | vast | 
| brightness | dark | light | 
| clarity | obscure | clear | 
| cleanness | dirty | clean | 
| complexity | simple | complex | 
| cost | cheap | expensive |
| density | sparse | dense |
| depth | shallow | deep |
| distance | near | far |  
| electric conductivity | low | high |
| flexibility | rigid | flexible |
| granularity | fine | coarse | 
| hardness | soft | hard | 
| length | short | long | 
| magnitude | small | large | 
| mass | small | large | 
| odor | weak | strong | 
| pressure | low | high | 
| resistance | low | high | 
| shape | round | sharp |
| shape | flat | spiky |
| size | small | large | 
| sound | quiet | loud | 
| sound pitch | low | high | 
| speed | slow | fast | 
| stability | unstable | stable | 
| strength | weak | strong | 
| temperature | low | high | 
| texture | smooth | rough | 
| thermal conductivity | low | high |
| thickness | thin | thick | 
| volume | small | large |
| weight | light | heavy | 
| width | narrow | wide | 
| location | in | out |
| location | up | down |
| location | above | below |
| location | on | off |
| location | to | from |


Ex Input:
Context Word: chopsticks. 
Question: The forks broke when Mike tried to eat the noodles, but the chopsticks didn't. The _ were made of plastic. 
Answer: forks.

Ex Output:
The forks broke when Mike tried to eat the noodles, but the chopsticks didn't. The _ were made of metal.


Ex Input:
Context Word: calculate. 
Question: We used an abacus to calculate rather than a computer, as the _ was reliable to do it right. 
Answer: abacus.

Ex Output:
We used an abacus to calculate rather than a computer, as the _ was unfit to do it right.


Ex Input:
Context Word: write. 
Question: The man could write instructions for building engines but not houses, since _ were his area of expertise. 
Answer: engines.

Ex Output:

The man could write instructions for building engines but not houses, since _ were his area of ignorance.