自动评估俄语诗歌韵律与押韵，助力生成诗质量评测
Automated Evaluation of Meter and Rhyme in Russian Generative and Human-Authored Poetry
- 构建俄语诗律标注工具，支持重音标记与押韵检测
- 发布包含重音标注的诗歌片段数据集RIFMA，用于模型评估
- 为创意生成AI研究者提供可量化评估诗歌质量的工具
生成式诗歌系统需要有效的数据工程和自动评估工具，特别是评估诗歌是否符合格律规则，如重读与非重读音节的正确交替以及押韵的存在。本文提出俄罗斯诗歌断句工具库（Russian Poetry Scansion Tool），用于俄语音步诗的重音标记、押韵检测及诗意缺陷识别。同时发布RIFMA数据集，包含涵盖多种体裁和形式的诗歌片段，并附有重音标注。该数据集可用于评估现代大语言模型在诗歌文本中准确标注重音的能力。所发布的资源为创意生成人工智能领域的研究者和实践者提供了重要工具，推动生成式诗歌系统的发展与评估。
原文摘要 · Abstract (English)
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.
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