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, 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：变音符号标注在提升阿拉伯语文本可读性与消除语义歧义方面具有关键作用。现有研究主要集中于为每个可标注字符添加完整变音符号（全标注）。相对而言，部分标注（PD）作为辅助理解的选择性标注方法尚未得到充分关注。研究表明，过量的变音符号会阻碍熟练读者的阅读效率——降低阅读速度与准确性。我们通过行为实验证明，部分标注文本通常比全标注文本更易阅读，有时甚至优于无标注文本。基于此，我们提出上下文对比部分标注（CCPD）——一种能与现有阿拉伯语变音标注系统无缝集成的新型PD方法。CCPD对每个单词进行两次处理（分别结合上下文与脱离上下文），仅对两次推理结果存在差异的字符进行标注。此外，我们提出了衡量部分标注质量的新指标，这对将该任务确立为机器学习问题至关重要。最后，我们提出TD2——一种基于Transformer的改进模型，该模型在我们提出的指标上展现出与所有已知系统截然不同的性能特征。