"I am a Speculation of the Poet"
— a neural network
This is my entry for the 2018 version of National Novel Generation Month, which challenges people to write a computer program that creates a 50,000-word novel.
Short version: I split each chapter of Moby-Dick into sentences, then had an AI choose the order in which the sentences should appear. I call the result Mboy-Dcki.
Take, for instance, the opening of the famous chapter on "The Whiteness of the Whale." Here is Melville's version:
WHAT the white whale was to Ahab, has been hinted; what, at times, he was to me, as yet remains unsaid.
Aside from those more obvious considerations touching Moby Dick, which could not but occasionally awaken in any man's soul some alarm, there was another thought, or rather vague, nameless horror concerning him, which at times by its intensity completely overpowered all the rest; and yet so mystical and well nigh ineffable was it, that I almost despair of putting it in a comprehensible form. It was the whiteness of the whale that above all things appalled me. But how can I hope to explain myself here; and yet, in some dim, random way, explain myself I must, else all these chapters might be naught.
Here is what my neural network put together:
What the white whale was to Ahab, has been hinted; what, at times, he was to me, as yet remains unsaid.
Bethink thee of the albatross, whence come those clouds of spiritual wonderment and pale dread, in which that white phantom sails in all imaginations? Wonder ye then at the fiery hunt? To analyse it, would seem impossible. The flashing cascade of his mane, the curving comet of his tail, invested him with housings more resplendent than gold and silver-beaters could have furnished him. He was the elected Xerxes of vast herds of wild horses, whose pastures in those days were only fenced by the Rocky Mountains and the Alleghanies.
This project is inspired by a passage in Benjamin Franklin's Autobiography in which he describes how he learned to write as a young man.
About this time I met with an odd volume of the Spectator. It was the third. I had never before seen any of them. I bought it, read it over and over, and was much delighted with it. I thought the writing excellent, and wished, if possible, to imitate it. With this view I took some of the papers, and, making short hints of the sentiment in each sentence, laid them by a few days, and then, without looking at the book, try'd to compleat the papers again, by expressing each hinted sentiment at length, and as fully as it had been expressed before, in any suitable words that should come to hand. Then I compared my Spectator with the original, discovered some of my faults, and corrected them. But I found I wanted a stock of words, or a readiness in recollecting and using them, which I thought I should have acquired before that time if I had gone on making verses; since the continual occasion for words of the same import, but of different length, to suit the measure, or of different sound for the rhyme, would have laid me under a constant necessity of searching for variety, and also have tended to fix that variety in my mind, and make me master of it. Therefore I took some of the tales and turned them into verse; and, after a time, when I had pretty well forgotten the prose, turned them back again. I also sometimes jumbled my collections of hints into confusion, and after some weeks endeavored to reduce them into the best order, before I began to form the full sentences and compleat the paper. This was to teach me method in the arrangement of thoughts.
Using a Recurrent Neural Network (RNN), I created an AI that performs a bastardized version of this unscrambling process. Rather than working with hints of the ideas underlying the prose, this program works with sentences. First, it splits a text into sentences, and then it tries to figure out what order those sentences should go in based only on the sentences themselves.
I trained the neural net on 500,000 sentences (with 10% held out for evaluation) from the Wright American Fiction corpus, excluding Moby-Dick itself. I then generated a new novel by having the AI decide the order of sentences within each chapter of Moby-Dick. You can view the result here.
Over all the chapters, the neural network is able to pick the right sentence ("right" meaning the one that appears next in the original text) about 3.5% of the time (328 / 9384 sentences chosen). (The success rate might be a little different for the actual text generator because of the constraint that each sentence only be used once.) This is not too impressive, but it is better than the expected success rate if the sentences were chosen randomly, which is 1.4%. Even when it makes a wrong decision, the model is not choosing sentences in a totally arbitrary way—it consistently ranks some sentences much higher than others.
Do these choices have any logic to them? I've yet to dig into it in detail, but I can offer some provisional observations. The network has learned to follow exclamation points with uncapitalized words, as was common in nineteenth-century novels; it also developed a rudimentary model of how quotation marks and dialogue tags fit together. It likes to follow one question with another. It also seems to have learned to group together sentences that contain similar pronouns (I/me, she/her, etc.). Whether it learned anything involving substantive words I do not know.
An additional experiment: this method is designed to work with two different training data sets: one (ideally very large) corpus is used to train the neural network to link sentences together, while a second corpus provides the sentences to link. If one wants to ensure that the network is really picking up on generalizable patterns it has learned, these two data sets should not overlap. However, it is possible to use the same corpus for both phases of the process. I tried training an alternative neural network solely on the text of Moby-Dick itself without any data held out for validation. The result is a badly overfit model that gets the right match 78% of the time because it has essentially memorized much of the specific language of the novel. Nonetheless, the generated results are interesting—much closer to the original novel than Mboy-Dcki, but still somewhat strange. Take the opening, for instance, in which Ishmael suggests that his spleen wants to go near the water:
Call me Ishmael. Some years ago--never mind how long precisely--having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation. Nothing will content them but the extremest limit of the land; loitering under the shady lee of yonder warehouses will not suffice. No. They must get just as nigh the water as they possibly can without falling in. And there they stand--miles of them--leagues. Inlanders all, they come from lanes and alleys, streets and avenues--north, east, south, and west. Yet here they all unite. Tell me, does the magnetic virtue of the needles of the compasses of all those ships attract them thither?
Since this neural network was trained on the same data set to which it was applied, it is essentially encoding information about the text in an inexact, lossy way and then reconstructing it. This version of the project captures the fact that Benjamin Franklin was not really coming at his "hints" with his mind a blank slate—however many weeks he may have waited, he still must have remembered something of the original essays he was trying to reconstruct.
This program employs a variant of the Markov chain method, one of the oldest and simplest methods of computational text generation. The most common version of this method works by measuring which words appear most often after particular sequences of words; it then uses this information to predict what word will come next. Thus, if the last 6 words are "What are your plans for the," the model might predict that the next word will be "weekend." Markov chains are typically constructed at the character, word, or token level. However, it is also possible to create a Markov chain model that operates at the sentence level. For each sentence generated, the model must assign probabilities for every sentence that could potentially come next. It is generally not possible to train such a model directly, since each sentence typically only occurs once even in a very large amount of text. However, it is possible to generate a sentence-level Markov model by using a neural net to determine the probabilities. That is, in essence, what this software does.
The neural net I created takes in the last 25 characters of a sentence and tries to predict the first 25 characters of the next sentence. I used a sequential model, meaning that the network produces the output one character at a time, with the probabilities for each new character conditioned by what characters have already been generated. Instead of using the network to generate text directly, the software uses it to compute a probability for every pair of sentences in a given text. After a bit of normalization, these probabilities can be used in a Markov chain text generator. To maximize the uncanny effect, I set up the generator to start with the actual first sentence of the chapter. After that, it always picks the next sentence with the highest probability according to the Markov model, only using each sentence once. This is a greedy algorithm, so it is not guaranteed to find the ordering with the highest overall probability. (There is an option to randomize the output as well.) I ran the generator separately for each chapter of Moby-Dick because it has to run a neural network on every pair of sentences, which would take too long for the whole novel.
The network architecture I used is similar to one of the simplest types that has been used in neural machine translation, an encoder-decoder network based on LSTM (Long Short-Term Memory) units. Instead of taking in a sentence and outputting a (supposed) translation, it takes in the last n characters of a sentence and outputs the predicted first n characters of the next sentence. As I explain in the code, I had to use a slightly different data format from the one used in machine translation to account for the fact that the input and output data do not represent complete linguistic units.
If you want to run the program yourself, you will need to install Python 3, NLTK, TensorFlow, and NumPy/SciPy. Then put your training data in a bunch of plain text files in a directory and the text you want to scramble in another plain text file. You can then do something like this at the command line:
python prepare_corpus.py <training-data-dir> <corpus-name>.corpus python train.py <corpus-name>.corpus <corpus-name>.network python markovize.py <corpus-name>.corpus <corpus-name>.network <input-text-name>.txt <output>.markov python markov_generate.py <input>.markov <output>.txt
The scripts contain various variables that you can edit to alter the behavior of the model.
The training and Markovizing scripts may take hours or days to run on an ordinary computer, depending on the amount of data you are using.
In addition to the code, I included the trained neural network and Markov models I used in generating Mboy-Dcki. I also included the script generate_md.py, which automates running the process on every chapter of the novel and compute_success_rate.py, which I used to compute the statistics.
The text of Moby-Dick is taken from Project Gutenberg.