# Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin

arXiv: https://arxiv.org/abs/2404.18264 · HTML: https://mlsanity.com/paper/2404.18264/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [On Language Models for Creoles](https://mlsanity.com/paper/2109.06074/) (2021)
- [Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification](https://mlsanity.com/paper/2003.12450/) (2020)
- [PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English](https://mlsanity.com/paper/1912.03444/) (2019)
- [NaijaNER : Comprehensive Named Entity Recognition for 5 Nigerian Languages](https://mlsanity.com/paper/2105.00810/) (2021)
- [Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study](https://mlsanity.com/paper/2106.08686/) (2021)
- [Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin](https://mlsanity.com/paper/2010.11123/) (2020)
- [Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin](https://mlsanity.com/paper/2307.00382/) (2023)
- [Robust Neural Machine Translation: Modeling Orthographic and Interpunctual Variation](https://mlsanity.com/paper/2009.05460/) (2020)

## Built on by (most cited first, 3 of 3)

- [Does Generative AI speak Nigerian-Pidgin?: Issues about Representativeness and Bias for Multilingualism in LLMs](https://mlsanity.com/paper/2404.19442/) (2024)
- [Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participation](https://mlsanity.com/paper/2503.03186/) (2025)
- [Surfacing Subtle Stereotypes: A Multilingual, Debate-Oriented Evaluation of Modern LLMs](https://mlsanity.com/paper/2511.01187/) (2025)

## Abstract (arXiv)

Nigerian Pidgin is an English-derived contact language and is traditionally an oral language, spoken by approximately 100 million people. No orthographic standard has yet been adopted, and thus the few available Pidgin datasets that exist are characterised by noise in the form of orthographic variations. This contributes to under-performance of models in critical NLP tasks. The current work is the first to describe various types of orthographic variations commonly found in Nigerian Pidgin texts, and model this orthographic variation. The variations identified in the dataset form the basis of a phonetic-theoretic framework for word editing, which is used to generate orthographic variations to augment training data. We test the effect of this data augmentation on two critical NLP tasks: machine translation and sentiment analysis. The proposed variation generation framework augments the training data with new orthographic variants which are relevant for the test set but did not occur in the training set originally. Our results demonstrate the positive effect of augmenting the training data with a combination of real texts from other corpora as well as synthesized orthographic variation, resulting in performance improvements of 2.1 points in sentiment analysis and 1.4 BLEU points in translation to English.