A Context-Contrastive Inference Approach To Partial Diacritization
This addresses the readability issue for skilled Arabic readers by proposing a partial diacritization method, which is incremental as it builds on existing diacritization systems.
The paper tackled the problem of partial diacritization in Arabic text, showing through a behavioral experiment that partially marked text can improve readability over fully marked or plain text, and introduced a novel approach (CCPD) that integrates with existing systems to diacritize only characters with disparities between contextual and non-contextual inferences.
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.