用难度自适应课程学习提升歌词翻译质量
LyriCAR: A Difficulty-Aware Curriculum Reinforcement Learning Framework For Controllable Lyric Translation
- 根据难度动态调整训练任务，逐步提升模型能力
- 在中英歌词翻译上超越现有方法，减少近40%训练步数
- 适合需要高质量跨行韵律和段落连贯性的歌词生成场景
歌词翻译是一项需平衡多重音乐约束的挑战性任务。现有方法多依赖人工规则和句子级建模，难以内化音乐-语言模式，且在段落级任务中泛化能力受限，无法有效处理跨行连贯性和全局押韵。本文提出LyriCAR，一种完全无监督的可控歌词翻译框架。该框架引入难度感知课程设计者与自适应课程策略，通过渐进式复杂任务引导模型训练，实现资源高效分配、加速收敛并提升整体翻译质量。在英文到中文歌词翻译任务上的大量实验表明，LyriCAR在标准翻译指标和多维奖励评分上均达到当前最优表现，且自适应课程策略将训练步数减少近40%的同时保持卓越性能。代码、数据与模型可于https://github.com/rle27/LyriCAR获取。
原文摘要 · Abstract (English)
Lyric translation is a challenging task that requires balancing multiple musical constraints. Existing methods often rely on hand-crafted rules and sentence-level modeling, which restrict their ability to internalize musical-linguistic patterns and to generalize effectively at the paragraph level, where cross-line coherence and global rhyme are crucial. In this work, we propose LyriCAR, a novel framework for controllable lyric translation that operates in a fully unsupervised manner. LyriCAR introduces a difficulty-aware curriculum designer and an adaptive curriculum strategy, ensuring efficient allocation of training resources, accelerating convergence, and improving overall translation quality by guiding the model with increasingly complex challenges. Extensive experiments on the EN-ZH lyric translation task show that LyriCAR achieves state-of-the-art results across both standard translation metrics and multi-dimensional reward scores, surpassing strong baselines. Notably, the adaptive curriculum strategy reduces training steps by nearly 40% while maintaining superior performance. Code, data and model can be accessed at https://github.com/rle27/LyriCAR.
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