Style is an important concept in today's challenges in natural language generating. After the success in the field of image style transfer, the task of text style transfer became actual and attractive. Researchers are also interested in the tasks of style reproducing in generation of the poetic text. Evaluation of style reproducing in natural poetry generation remains a problem. I used 3 character-based LSTM-models to work with style reproducing assessment. All three models were trained on the corpus of texts by famous Russian-speaking poets. Samples were shown to the assessors and 4 answer options were offered, the style of which poet this sample reproduces. In addition, the assessors were asked how well they were familiar with the work of the poet they had named. Students studying history of literature were the assessors, 94 answers were received. It has appeared that accuracy of definition of style increases if the assessor can quote the poet by heart. Each model showed at least 0.7 macro-average accuracy. The experiment showed that it is better to involve a professional rather than a naive reader in the evaluation of style in the tasks of poetry generation, while lstm models are good at reproducing the style of Russian poets even on a limited training corpus.

翻译：风格是当前自然语言生成挑战中的一个重要概念。继图像风格迁移领域取得成功后，文本风格迁移任务变得实际且具有吸引力。研究人员也对诗歌文本生成中的风格复现任务感兴趣。自然诗歌生成中风格复现的评估仍是一个问题。我使用了三个基于字符的LSTM模型来处理风格复现评估。这三个模型均在著名俄语诗人文本语料库上进行了训练。样本呈现给评估者，并提供四个答案选项，以判断该样本模仿的是哪位诗人的风格。此外，评估者还被问及他们对自己所选诗人作品的熟悉程度。评估者为学习文学史的学生，共收到94份回答。结果表明，如果评估者能背诵诗人作品，风格识别的准确率会提高。每个模型均达到了至少0.7的宏观平均准确率。实验表明，在诗歌生成任务的风格评估中，最好邀请专业人士而非普通读者参与，而LSTM模型即使在有限的训练语料上也能很好地复现俄语诗人的风格。