  This paper addresses the problem of stylized text generation in a
multilingual setup. A version of a language model based on a long short-term
memory (LSTM) artificial neural network with extended phonetic and semantic
embeddings is used for stylized poetry generation. The quality of the resulting
poems generated by the network is estimated through bilingual evaluation
understudy (BLEU), a survey and a new cross-entropy based metric that is
suggested for the problems of such type. The experiments show that the proposed
model consistently outperforms random sample and vanilla-LSTM baselines, humans
also tend to associate machine generated texts with the target author.
