根据英语学习者水平优化句子简化，提升词汇覆盖率。
Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition
- 用强化学习让大模型自动生成符合学习者水平的简化句
- 目标级别词汇频率和多样性提升超20%，且简化质量高
- 无需平行语料，适合语言教学与自适应学习系统
文本简化对提高英语作为第二语言（ESL）学习者的可读性和理解力至关重要。本研究进一步提出通过简化促进学习者语言习得：在简化复杂句的同时，增加目标语言水平的词汇覆盖度。我们不依赖平行语料，而是基于大语言模型进行强化学习，采用词级与句级奖励机制，并通过自生成输出迭代训练模型，引导其搜索满足目标属性的简化方案。在CEFR-SP和TurkCorpus数据集上的实验表明，相比基线模型，该方法可使目标级别词汇的出现频率和多样性提升超过20%，同时保持高质量的简化效果。
原文摘要 · Abstract (English)
Text simplification is crucial for improving accessibility and comprehension for English as a Second Language (ESL) learners. This study goes a step further and aims to facilitate ESL learners' language acquisition by simplification. Specifically, we propose simplifying complex sentences to appropriate levels for learners while also increasing vocabulary coverage of the target level in the simplifications. We achieve this without a parallel corpus by conducting reinforcement learning on a large language model. Our method employs token-level and sentence-level rewards, and iteratively trains the model on its self-generated outputs to guide the model to search for simplification hypotheses that satisfy the target attributes. Experiment results on CEFR-SP and TurkCorpus datasets show that the proposed method can effectively increase the frequency and diversity of vocabulary of the target level by more than $20\%$ compared to baseline models, while maintaining high simplification quality.
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