Guess who?Multilingual approach for the automated generation of author-stylized poetry
Table 1: Parameters of the training datasets.
['[EMPTY]', '[BOLD] N. of  [BOLD] documents', '[BOLD] Size of  [BOLD] vocab.', '[BOLD] N. of  [BOLD] authors', '[BOLD] Size']
We have trained our model on two datasets of English and Russian poetry. The datasets were proprietary ones and were already available. All punctuation was deleted, every character was transferred to a lower case. No other preprocessing was made. During the training phase we tokenize the beginning and ending of every text Ti, so that in the generation phase the network is initialized with a special ’start’ token and is conditioned on values of document parameters S. In this paper we test the proposed mechanism for the stylized text generation with one categorical variable - the name of the author. We trained the model for English (running tests on lyrics of William Shakespeare, Edgar Allan Poe, Lewis Carroll, Oscar Wilde and Bob Marley as well as lyrics of the American band Nirvana and UK band Muse) and Russian (Alexander Pushkin, Sergey Esenin, Joseph Brodsky, Egor Letov and Zemfira Ramazanova). We want to emphasize that we do not see any excessive difficulties in implementation of the proposed model for other languages for which one can form a training corpus C and provide a phonetically transcribed vocabulary Vp.