对齐与闪耀：构建多语言文本简化任务的高质量句子对语料库
Align and Shine: Building High-Quality Sentence-Aligned Corpora for Multilingual Text Simplification
AI总结：
本文通过收集和处理可比语料库中的众包简化数据，构建了多语言文本简化任务的高质量句子对语料库，解决了其他语言缺乏大规模高质量数据的问题。
摘要：
文本简化在提高不同受众对书面信息的可访问性和可理解性方面起着至关重要的作用，包括语言学习者和识字能力有限的读者。尽管其重要性，但除英语外，其他语言的大型高质量数据集仍稀缺。本文报告了通过收集和处理可比语料库中的众包简化数据，构建适用于多种语言（加泰罗尼亚语、英语、法语、意大利语和西班牙语）的文本简化系统训练和测试语料库的实验研究。我们报告了从文档级数据中进行句子级对齐的机制。所得到的对齐句子对数据集已公开可用。
英文摘要：
Text simplification plays a crucial role in improving the accessibility and comprehensibility of written information for diverse audiences, including language learners and readers with limited literacy. Despite its importance, large-scale, high-quality datasets for training and evaluating text simplification models remain scarce for languages other than English. This paper reports an experimental study on the collection and processing of crowd-sourced simplification data from comparable corpora to construct a corpus suitable for both training and testing text simplification systems across multiple languages (Catalan, English, French, Italian and Spanish). We report mechanisms for sentence-level alignment from document-level data. The resulting dataset of the aligned sentence pairs is publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
