# Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector

arXiv: https://arxiv.org/abs/2305.08518 · HTML: https://mlsanity.com/paper/2305.08518/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Fully Character-Level Neural Machine Translation without Explicit Segmentation](https://mlsanity.com/paper/1610.03017/) (2016)
- [A Survey of Deep Learning Techniques for Neural Machine Translation](https://mlsanity.com/paper/2002.07526/) (2020)
- [Grammatical Error Correction in Low-Resource Scenarios](https://mlsanity.com/paper/1910.00353/) (2019)
- [How Much Does Tokenization Affect Neural Machine Translation?](https://mlsanity.com/paper/1812.08621/) (2018)
- [Optimizing Transformer for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2011.02266/) (2020)
- [Challenges of Computational Processing of Code-Switching](https://mlsanity.com/paper/1610.02213/) (2016)
- [Low-Resourced Machine Translation for Senegalese Wolof Language](https://mlsanity.com/paper/2305.00606/) (2023)

## Built on by (most cited first, 4 of 4)

- [Contextual Spelling Correction with Language Model for Low-resource Setting](https://mlsanity.com/paper/2404.18072/) (2024)
- [Task-Oriented Dialog Systems for the Senegalese Wolof Language](https://mlsanity.com/paper/2412.11203/) (2024)
- [Sentiment Analysis on the young people's perception about the mobile Internet costs in Senegal](https://mlsanity.com/paper/2504.13284/) (2025)
- [Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research](https://mlsanity.com/paper/2601.09716/) (2025)

## Abstract (arXiv)

The progress of Natural Language Processing (NLP), although fast in recent years, is not at the same pace for all languages. African languages in particular are still behind and lack automatic processing tools. Some of these tools are very important for the development of these languages but also have an important role in many NLP applications. This is particularly the case for automatic spell checkers. Several approaches have been studied to address this task and the one modeling spelling correction as a translation task from misspelled (noisy) text to well-spelled (correct) text shows promising results. However, this approach requires a parallel corpus of noisy data on the one hand and correct data on the other hand, whereas Wolof is a low-resource language and does not have such a corpus. In this paper, we present a way to address the constraint related to the lack of data by generating synthetic data and we present sequence-to-sequence models using Deep Learning for spelling correction in Wolof. We evaluated these models in three different scenarios depending on the subwording method applied to the data and showed that the latter had a significant impact on the performance of the models, which opens the way for future research in Wolof spelling correction.