用AI生成符合古典阿拉伯诗韵律的诗句，兼顾节奏与语义。
A Rhythm-Aware Phrase Insertion for Classical Arabic Poetry Composition
- 基于字节级Transformer的条件去噪训练，让模型按指定韵律填词。
- 在诗歌数据集上实现高韵律匹配度，同时保持语义连贯。
- 适合对阿拉伯语诗歌创作或跨语言生成感兴趣的开发者与学者。
本文提出一种在阿拉伯诗歌中插入短语以符合特定韵律的方法，采用基于字节的多语言Transformer模型ByT5。研究设计了针对完全注音阿拉伯文的规则化音素到节拍转换机制，用于提取韵律特征。通过条件去噪目标微调ByT5，使模型在掩码词语重建时匹配目标韵律。采用课程学习策略，先在通用阿拉伯语数据集上预训练，再在诗歌数据集上微调，并探索从英语到阿拉伯语的跨语言迁移。实验表明，该模型在保持语义连贯性的前提下，实现了较高的韵律一致性，具备用于古典阿拉伯诗歌共创应用的潜力。
原文摘要 · Abstract (English)
This paper presents a methodology for inserting phrases in Arabic poems to conform to a specific rhythm using ByT5, a byte-level multilingual transformer-based model. Our work discusses a rule-based grapheme-to-beat transformation tailored for extracting the rhythm from fully diacritized Arabic script. Our approach employs a conditional denoising objective to fine-tune ByT5, where the model reconstructs masked words to match a target rhythm. We adopt a curriculum learning strategy, pre-training on a general Arabic dataset before fine-tuning on poetic dataset, and explore cross-lingual transfer from English to Arabic. Experimental results demonstrate that our models achieve high rhythmic alignment while maintaining semantic coherence. The proposed model has the potential to be used in co-creative applications in the process of composing classical Arabic poems.
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