The whole point of the T5 paper was showing that purely by prepending a prefix multiple distinct tasks could be done, using the same model architecture, to close to SOTA levels.
That leads us to your question: can your problem be done with T5? The answer is yeah, probably.
As to how to format the input for this task I’d probably try the following:
If we have the following input:
Input: {‘context’: ‘food topics’, ‘sentence’:‘sushi is a great dessert’}
Then I’d convert it into the following:
Processed Input: f"summarize: context: {context}; sentence: {sentence}"
(So: f"summarize: context: food topics; sentence: sushi is a great dessert")
The target is of course your paraphrase.
This way you separate context and sentence for the model, a separation which it should eventually learn with enough training examples. Also, I’ve reused the “summarize” keyword from T5, since it is vaguely similar to this task and might help a bit (especially initially).
Anyways this should work given enough training examples. Good Luck.