What's the chance that these limericks are now in the training set? As others mention, it'd be interesting to come up with a way to synthesize something sufficiently interesting so it always evades training fit.
One could also programmatically (e.g. with nltk or spacy, replace nouns, named entities, etc) modify the dataset, even up to the point that every test run is unique.
You could also throw in vector similarity if you wanted to keep words as more synonyms or antonyms.
