# A Multitask Learning Approach for Diacritic Restoration

arXiv: https://arxiv.org/abs/2006.04016 · HTML: https://mlsanity.com/paper/2006.04016/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Multi-Task Learning in Natural Language Processing: An Overview](https://mlsanity.com/paper/2109.09138/) (2021)
- [Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization](https://mlsanity.com/paper/2406.05760/) (2024)
- [Improving Arabic Diacritization by Learning to Diacritize and Translate](https://mlsanity.com/paper/2109.14150/) (2021)
- [Correcting diacritics and typos with a ByT5 transformer model](https://mlsanity.com/paper/2201.13242/) (2022)
- [MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection](https://mlsanity.com/paper/2402.08982/) (2024)
- [Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need](https://mlsanity.com/paper/2401.04848/) (2024)
- [Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text](https://mlsanity.com/paper/2306.03557/) (2023)
- [A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2502.03827/) (2025)
- [A Context-Contrastive Inference Approach To Partial Diacritization](https://mlsanity.com/paper/2401.08919/) (2024)
- [Proper Noun Diacritization for Arabic Wikipedia: A Benchmark Dataset](https://mlsanity.com/paper/2505.02656/) (2025)
- [Accent Placement Models for Rigvedic Sanskrit Text](https://mlsanity.com/paper/2511.23088/) (2025)
- [Diacritic Restoration for Low-Resource Indigenous Languages: Case Study with Bribri and Cook Islands M=aori](https://mlsanity.com/paper/2512.19630/) (2025)

## Abstract (arXiv)

In many languages like Arabic, diacritics are used to specify pronunciations as well as meanings. Such diacritics are often omitted in written text, increasing the number of possible pronunciations and meanings for a word. This results in a more ambiguous text making computational processing on such text more difficult. Diacritic restoration is the task of restoring missing diacritics in the written text. Most state-of-the-art diacritic restoration models are built on character level information which helps generalize the model to unseen data, but presumably lose useful information at the word level. Thus, to compensate for this loss, we investigate the use of multi-task learning to jointly optimize diacritic restoration with related NLP problems namely word segmentation, part-of-speech tagging, and syntactic diacritization. We use Arabic as a case study since it has sufficient data resources for tasks that we consider in our joint modeling. Our joint models significantly outperform the baselines and are comparable to the state-of-the-art models that are more complex relying on morphological analyzers and/or a lot more data (e.g. dialectal data).