# Guess who? Multilingual approach for the automated generation of author-stylized poetry

arXiv: https://arxiv.org/abs/1807.07147 · HTML: https://mlsanity.com/paper/1807.07147/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 10 of 10)

- [A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models](https://mlsanity.com/paper/2201.05337/) (2022)
- [The AI Ghostwriter Effect: When Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors](https://mlsanity.com/paper/2303.03283/) (2023)
- [Stylized Story Generation with Style-Guided Planning](https://mlsanity.com/paper/2105.08625/) (2021)
- [StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing](https://mlsanity.com/paper/2208.13423/) (2022)
- [Creative Data Generation: A Review Focusing on Text and Poetry](https://mlsanity.com/paper/2305.08493/) (2023)
- [DYPLODOC: Dynamic Plots for Document Classification](https://mlsanity.com/paper/2107.12226/) (2021)
- [LyricJam: A system for generating lyrics for live instrumental music](https://mlsanity.com/paper/2106.01960/) (2021)
- [Actionable Entities Recognition Benchmark for Interactive Fiction](https://mlsanity.com/paper/2109.13855/) (2021)
- [A pattern recognition approach for distinguishing between prose and poetry](https://mlsanity.com/paper/2107.08512/) (2021)
- [Designing Mixed-Initiative Video Games](https://mlsanity.com/paper/2307.03877/) (2023)

## Abstract (arXiv)

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.