# A Context-Contrastive Inference Approach To Partial Diacritization

arXiv: https://arxiv.org/abs/2401.08919 · HTML: https://mlsanity.com/paper/2401.08919/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [A Multitask Learning Approach for Diacritic Restoration](https://mlsanity.com/paper/2006.04016/) (2020)
- [Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation](https://mlsanity.com/paper/1911.03531/) (2019)
- [Deep Diacritization: Efficient Hierarchical Recurrence for Improved Arabic Diacritization](https://mlsanity.com/paper/2011.00538/) (2020)
- [Camelira: An Arabic Multi-Dialect Morphological Disambiguator](https://mlsanity.com/paper/2211.16807/) (2022)
- [Efficient Convolutional Neural Networks for Diacritic Restoration](https://mlsanity.com/paper/1912.06900/) (2019)
- [Arabic Diacritic Recovery Using a Feature-Rich biLSTM Model](https://mlsanity.com/paper/2002.01207/) (2020)
- [Homograph Disambiguation Through Selective Diacritic Restoration](https://mlsanity.com/paper/1912.04479/) (2019)
- [Hybrid approaches for automatic vowelization of Arabic texts](https://mlsanity.com/paper/1410.2646/) (2014)

## Built on by (most cited first, 1 of 1)

- [Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization](https://mlsanity.com/paper/2406.05760/) (2024)

## Abstract (arXiv)

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.