NLP text autoencoder that generates text in poetic meter
A starting point that comes to mind is creating a cost function for a sentence being in IP. Now, while normally this is a binary affair (either a sentence is in IP or not - or so I would assume), this does not lend itself readily to the task. You should devise a cost  function that measures how close your sentence is to IP (so I assume that sentence with 11 syllables would have a lower cost than one with 12. Verbal stresses and their associated costs are left to you to figure out ;) ).

After you have a cost function, you should take some sort of pretrained deep neural network that can translate from one language to another, such as those that use Word2Vec, set it to translate from English to English, and train it with two costs - one for keeping the meaning of the text (probably with another instance of word2vec or something to that effect), the other for being in IP. The relative weights of the two costs should be determined experimentally (you may even want to change them while training).  

This is of course a shot in the dark. More research and experimentation will be required, but I hope this may serve you as a starting point in your endeavor.

Be advised that while this is an intriguing project, in my opinion it might not be the best choice for beginners.