I would like to create an NLP autoencoder that happens to only generate text that conforms to a poetic meter, for example 'iambic pentameter'. That is, the output should be a series of clauses which are 10 syllables long and are read aloud with the verbal stresses as 'duh-DUH duh-DUH duh-DUH duh-DUH duh-DUH'. Shakespeare and his contemporaries used this format for much of their poetry.
The only method I can think of is to collect lines of text which are in this format, and then train an autoencoder on these. Since the network only emits iambic pentameter while training, it should also emit iambic pentameter while predicting. Given a large enough corpus, this seems like it should work.
One problem is that this training only accepts iambic pentameter. To rewrite other text into IP, the training needs to include variations of text in IP and train (not IP sentence) to emit (IP sentence). Generating these variations is straightforward. This crosses the line from training "sentence embeddings" to training "thought embeddings".
Are there other ways to solve this problem? Is there a way to directly alter the sentence embedding space? For example, variational sampling works by applying a nonlinear transformation on the embedding space.
Note: the CMU Pronouncing Dictionary supplies pronunciation stresses for over 100k words, usable for classifying meter: http://www.speech.cs.cmu.edu/cgi-bin/cmudict
Note: this is a personal hobby project to teach myself deep learning & NLP- I really do not know whether it is achievable with current NLP technology.