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 |


Example Input: Context Word: event. 
Question: Joe had two events to go to at 4PM and 6PM so he's cutting it close. Luckily, the _ event was cancelled. 
Answer: 4PM.
Example Output: Joe had two events to go to at 4PM and 6PM so he's cutting it close. Luckily, the _ event started late.

Example Input: Context Word: soups. 
Question: When going out to eat, Carmen cares less about the soups than he does about the desserts. The _ are more generic to him. 
Answer: soups.
Example Output: When going out to eat, Carmen cares less about the soups than he does about the desserts. The _ are more unique to him.

Example Input: Context Word: necktie. 
Question: Tony needed a new necktie for his interview tomorrow, and the nearest store only offered a bowtie. Luckily, he found the _ . 
Answer: necktie.
Example Output:
Tony needed a new necktie for his interview tomorrow, and the nearest store only offered a bowtie. Luckily, he traded the _ .