# Low-Resourced Machine Translation for Senegalese Wolof Language

arXiv: https://arxiv.org/abs/2305.00606 · HTML: https://mlsanity.com/paper/2305.00606/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [Beyond BLEU: Training Neural Machine Translation with Semantic Similarity](https://mlsanity.com/paper/1909.06694/) (2019)
- [Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go](https://mlsanity.com/paper/2203.08351/) (2022)
- [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)

## Built on by (most cited first, 5 of 5)

- [Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector](https://mlsanity.com/paper/2305.08518/) (2023)
- [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)
- [MudawanSn: A Gold-Standard Wolof-Arabic Parallel Corpus for Machine Translation](https://mlsanity.com/paper/2609.17539/) (2026)

## Abstract (arXiv)

Natural Language Processing (NLP) research has made great advancements in recent years with major breakthroughs that have established new benchmarks. However, these advances have mainly benefited a certain group of languages commonly referred to as resource-rich such as English and French. Majority of other languages with weaker resources are then left behind which is the case for most African languages including Wolof. In this work, we present a parallel Wolof/French corpus of 123,000 sentences on which we conducted experiments on machine translation models based on Recurrent Neural Networks (RNN) in different data configurations. We noted performance gains with the models trained on subworded data as well as those trained on the French-English language pair compared to those trained on the French-Wolof pair under the same experimental conditions.