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Say you want to translate a sentence from language A to language B. You have a system that generates 10 possible translations in language B. Now you use a language model of language B to figure out which is the best translation by asking the language model which of the translations has the highest probability of being generated by the model.

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Is there any way to "translate" between writing styles of the same language? I'm thinking something analogous to the Van Gogh-ify image-processing techniques using deep convolutional networks.

Even very simple transformations, e.g., adding alliteration/assonance or adding rhymes everywhere, might be fun.

Look into Google's SyntaxNet - https://research.googleblog.com/2016/05/announcing-syntaxnet...

Should help you break down sentences into their semantic parts. The transformations are then made by walking the syntax tree and modifying the tagged parts of speech as you see fit.

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