Diacritization plays a pivotal role in improving readability and disambiguating the meaning of Arabic texts. Efforts have so far focused on marking every eligible character (Full Diacritization). Comparatively overlooked, Partial Diacritzation (PD) is the selection of a subset of characters to be marked to aid comprehension where needed. Research has indicated that excessive diacritic marks can hinder skilled readers--reducing reading speed and accuracy. We conduct a behavioral experiment and show that partially marked text is often easier to read than fully marked text, and sometimes easier than plain text. In this light, we introduce Context-Contrastive Partial Diacritization (CCPD)--a novel approach to PD which integrates seamlessly with existing Arabic diacritization systems. CCPD processes each word twice, once with context and once without, and diacritizes only the characters with disparities between the two inferences. Further, we introduce novel indicators for measuring partial diacritization quality (SR, PDER, HDER, ERE), essential for establishing this as a machine learning task. Lastly, we introduce TD2, a Transformer-variant of an established model which offers a markedly different performance profile on our proposed indicators compared to all other known systems.

翻译：变音符号标注在提高阿拉伯文本可读性和消除歧义方面起着关键作用。此前的努力主要集中在标记每个符合条件的字符（全变音符号标注）上。相对被忽视的部分变音符号标注（PD）则选择标记部分字符，以在需要时辅助理解。研究表明，过多的变音符号可能阻碍熟练读者的阅读——降低阅读速度和准确性。我们通过一项行为实验证明，部分标记文本通常比全标记文本更易读，有时甚至比无标记文本更易读。基于此，我们提出了上下文对比部分变音符号标注（CCPD）——一种与现有阿拉伯语变音符号系统无缝集成的PD新方法。CCPD对每个词处理两次，一次带上下文，一次不带上下文，仅标注两次推理结果存在差异的字符。此外，我们引入了衡量部分变音符号标注质量的新指标（SR、PDER、HDER、ERE），这对于将其确立为机器学习任务至关重要。最后，我们提出了TD2，这是一种基于现有模型的Transformer变体，在我们的新指标上其性能表现与所有其他已知系统显著不同。