# Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yor`ub'a Language Text

arXiv: https://arxiv.org/abs/1804.00832 · HTML: https://mlsanity.com/paper/1804.00832/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 11 of 11)

- [The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation](https://mlsanity.com/paper/2110.03036/) (2021)
- [OkwuGb'e: End-to-End Speech Recognition for Fon and Igbo](https://mlsanity.com/paper/2103.07762/) (2021)
- [Diacritics Restoration using BERT with Analysis on Czech language](https://mlsanity.com/paper/2105.11408/) (2021)
- [Correcting diacritics and typos with a ByT5 transformer model](https://mlsanity.com/paper/2201.13242/) (2022)
- [YAD: Leveraging T5 for Improved Automatic Diacritization of Yor`ub'a Text](https://mlsanity.com/paper/2412.20218/) (2024)
- [AfriVEC: Word Embedding Models for African Languages. Case Study of Fon and Nobiin](https://mlsanity.com/paper/2103.05132/) (2021)
- [Bridging Gaps in Natural Language Processing for Yor`ub'a: A Systematic Review of a Decade of Progress and Prospects](https://mlsanity.com/paper/2502.17364/) (2025)
- [Are LLMs Good Text Diacritizers? An Arabic and Yoruba Case Study](https://mlsanity.com/paper/2506.11602/) (2025)
- [NaijaNLP: A Survey of Nigerian Low-Resource Languages](https://mlsanity.com/paper/2502.19784/) (2025)
- [Beyond Word Error Rate: A Switch Aware Evaluation of ASR and Audio Language Models on English Yoruba Code-Switched Speech](https://mlsanity.com/paper/2609.11786/) (2026)
- [Yo-ByT5: Efficient and High-Fidelity Diacritic Restoration for Yor`ub'a](https://mlsanity.com/paper/2610.01634/) (2026)

## Abstract (arXiv)

Yor`ub'a is a widely spoken West African language with a writing system rich in tonal and orthographic diacritics. With very few exceptions, diacritics are omitted from electronic texts, due to limited device and application support. Diacritics provide morphological information, are crucial for lexical disambiguation, pronunciation and are vital for any Yor`ub'a text-to-speech (TTS), automatic speech recognition (ASR) and natural language processing (NLP) tasks. Reframing Automatic Diacritic Restoration (ADR) as a machine translation task, we experiment with two different attentive Sequence-to-Sequence neural models to process undiacritized text. On our evaluation dataset, this approach produces diacritization error rates of less than 5%. We have released pre-trained models, datasets and source-code as an open-source project to advance efforts on Yor`ub'a language technology.