Learning Literary Style End-to-end with Artificial Neural Networks

Abstract

This paper addresses the generation of stylized texts in a multilingual setup. A long short-term memory (LSTM) language model with extended phonetic and semantic embeddings is shown to capture poetic style when trained end-to-end without any expert knowledge. Phonetics seems to have a comparable contribution to the overall model performance as the information on the target author. The quality of the generated texts is estimated through bilingual evaluation understudy (BLEU), a new cross-entropy based metric, and a survey of human peers. When a style of target author is recognized by the humans, they do not seem to distinguish generated texts and originals.

Music generation with variational recurrent autoencoder supported by history

“…Due to the fact that we are not interested in the recall of the obtained classifier (when working with neural generative models one often faces an excessive amount of generated melodies, yet wants to filter more pleasing ones), one can make such heuristics even more strict so that 100 % accuracy is achieved. A similar approach was used in [25] for text generation and in [22] for drum pattern sampling and proved itself useful. We believe that such filtering could be adopted across various generative tasks and can significantly improve the resulting quality at a relatively low development cost.…”