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 per formance profile on our proposed indicators compared to all other known systems.

翻译：变音符号化在提升阿拉伯语文本可读性和消除歧义中起着关键作用。现有研究主要关注标记所有可标注字符（全变音符号化）。相比之下，部分变音符号化（PD）——即选择标记部分字符以辅助必要理解——受到的关注较少。研究表明，过多的变音符号会阻碍熟练读者的阅读速度与准确性。我们通过行为实验证明，部分标记文本通常比全标记文本更易读，有时甚至优于无标记文本。基于此，我们提出上下文对比部分变音符号化（CCPD）——一种可与现有阿拉伯语变音系统无缝集成的PD新方法。CCPD对每个单词进行两次处理（一次带上下文，一次不带），仅对两次推理结果存在差异的字符进行变音标注。此外，我们引入了衡量部分变音符号化质量的新指标（SR、PDER、HDER、ERE），这对将该任务确立为机器学习任务至关重要。最后，我们提出了TD2——一种基于现有模型的Transformer变体，该模型在建议指标上的性能表现显著不同于所有已知系统。