# Automatic Correction of Writing Anomalies in Hausa Texts

arXiv: https://arxiv.org/abs/2506.03820 · HTML: https://mlsanity.com/paper/2506.03820/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 66 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 30 of 31)

- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [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)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task](https://mlsanity.com/paper/2410.03983/) (2024)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [Advocating Character Error Rate for Multilingual ASR Evaluation](https://mlsanity.com/paper/2410.07400/) (2024)
- [Cheetah: Natural Language Generation for 517 African Languages](https://mlsanity.com/paper/2401.01053/) (2024)
- [NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification](https://mlsanity.com/paper/2305.10971/) (2023)
- [Correcting FLORES Evaluation Dataset for Four African Languages](https://mlsanity.com/paper/2409.00626/) (2024)
- [HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language](https://mlsanity.com/paper/2305.17690/) (2023)
- [AFRIDOC-MT: Document-level MT Corpus for African Languages](https://mlsanity.com/paper/2501.06374/) (2025)
- [Are Generative Language Models Multicultural? A Study on Hausa Culture and Emotions using ChatGPT](https://mlsanity.com/paper/2406.19504/) (2024)
- [The State of Large Language Models for African Languages: Progress and Challenges](https://mlsanity.com/paper/2506.02280/) (2025)
- [HausaMT v1.0: Towards English-Hausa Neural Machine Translation](https://mlsanity.com/paper/2006.05014/) (2020)
- [Does Generative AI speak Nigerian-Pidgin?: Issues about Representativeness and Bias for Multilingualism in LLMs](https://mlsanity.com/paper/2404.19442/) (2024)
- [Text normalization for low-resource languages: the case of Ligurian](https://mlsanity.com/paper/2206.07861/) (2022)
- [Investigating the Impact of Language-Adaptive Fine-Tuning on Sentiment Analysis in Hausa Language Using AfriBERTa](https://mlsanity.com/paper/2501.11023/) (2025)
- [AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text](https://mlsanity.com/paper/2503.18247/) (2025)
- [HausaNLP at SemEval-2023 Task 12: Leveraging African Low Resource TweetData for Sentiment Analysis](https://mlsanity.com/paper/2304.13634/) (2023)
- [Analyzing the Use of Character-Level Translation with Sparse and Noisy Datasets](https://mlsanity.com/paper/2109.13723/) (2021)
- [HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing](https://mlsanity.com/paper/2505.14311/) (2025)

## Abstract (arXiv)

Hausa texts are often characterized by writing anomalies, such as incorrect character substitutions and spacing errors, which sometimes hinder natural language processing (NLP) applications. This paper presents an approach to automatically correct anomalies by finetuning transformer-based models. Using a corpus gathered from several public sources, we create a large-scale parallel dataset of over 400,000 noisy-clean Hausa sentence pairs by introducing synthetically generated noise to mimic realistic writing errors. In addition, we finetune several multilingual and African language models, including M2M100, AfriTeVA, NCAIR1/N-ATLaS, UBC-NLP/cheetah-base, and other variants of BART and T5 for this correction task. Our experimental results demonstrate that models such as M2M100 achieve state-of-the-art results despite their smaller size and distinct pretraining, and that correcting errors can have a significant impact in improving downstream tasks such as text classification, machine translation, question answering, and LLM prompting in general. This research provides a methodology, a publicly available dataset, and a comparison of models to improve Hausa text quality, thereby advancing NLP capabilities for the language and offering transferable insights for other low-resource languages.