# Automated Evaluation of Meter and Rhyme in Russian Generative and Human-Authored Poetry

arXiv: https://arxiv.org/abs/2502.20931 · HTML: https://mlsanity.com/paper/2502.20931/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing](https://mlsanity.com/paper/2310.08433/) (2023)
- [Chinese Poetry Generation with Planning based Neural Network](https://mlsanity.com/paper/1610.09889/) (2016)
- [Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets](https://mlsanity.com/paper/2406.18906/) (2024)
- [Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features](https://mlsanity.com/paper/2102.08858/) (2021)
- [Prose2Poem: The Blessing of Transformers in Translating Prose to Persian Poetry](https://mlsanity.com/paper/2109.14934/) (2021)
- [Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning](https://mlsanity.com/paper/2111.07592/) (2021)
- [PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation](https://mlsanity.com/paper/2306.08456/) (2023)
- [SP-GPT2: Semantics Improvement in Vietnamese Poetry Generation](https://mlsanity.com/paper/2110.15723/) (2021)
- [GPT Czech Poet: Generation of Czech Poetic Strophes with Language Models](https://mlsanity.com/paper/2407.12790/) (2024)

## Built on by (most cited first, 1 of 1)

- [Neo-Classic: A Benchmark for Evaluating Linguistic-Aesthetic Reasoning in Classical Chinese Poetry](https://mlsanity.com/paper/2609.19154/) (2026)

## Abstract (arXiv)

Generative poetry systems require effective tools for data engineering and automatic evaluation, particularly to assess how well a poem adheres to versification rules, such as the correct alternation of stressed and unstressed syllables and the presence of rhymes. In this work, we introduce the Russian Poetry Scansion Tool library designed for stress mark placement in Russian-language syllabo-tonic poetry, rhyme detection, and identification of defects of poeticness. Additionally, we release RIFMA -- a dataset of poem fragments spanning various genres and forms, annotated with stress marks. This dataset can be used to evaluate the capability of modern large language models to accurately place stress marks in poetic texts. The published resources provide valuable tools for researchers and practitioners in the field of creative generative AI, facilitating advancements in the development and evaluation of generative poetry systems.