I split each chapter of Moby-Dick into sentences, then used a neural network to try to guess what order the sentences should appear in. I call the result Mboy-Dcki.
This is essentially a Markov chain model that works at the level of sentences rather than words or tokens. Such a model cannot be trained directly, so I created a encoder-decoder-type recurrent neural network that takes in the last 25 characters of a sentence and tries to guess what the first 25 characters of the next sentence will be. I then used this network to compute the probabilities for each pair of sentences.
It actually sort of works—at the very least, it picks the right sentence a little more often than chance would dictate. But the point, of course, is in the interesting ways it fails.
Code and a more detailed explanation are here.
I split each chapter of Moby-Dick into sentences, then used a neural network to try to guess what order the sentences should appear in. I call the result Mboy-Dcki.
This is essentially a Markov chain model that works at the level of sentences rather than words or tokens. Such a model cannot be trained directly, so I created a encoder-decoder-type recurrent neural network that takes in the last 25 characters of a sentence and tries to guess what the first 25 characters of the next sentence will be. I then used this network to compute the probabilities for each pair of sentences.
It actually sort of works—at the very least, it picks the right sentence a little more often than chance would dictate. But the point, of course, is in the interesting ways it fails.
Code and a more detailed explanation are here.