# Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creole

arXiv: https://arxiv.org/abs/2504.02674 · HTML: https://mlsanity.com/paper/2504.02674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Why do language models perform worse for morphologically complex languages?](https://mlsanity.com/paper/2411.14198/) (2024)
- [Are All Languages Equally Hard to Language-Model?](https://mlsanity.com/paper/1806.03743/) (2018)
- [Morphology Matters: A Multilingual Language Modeling Analysis](https://mlsanity.com/paper/2012.06262/) (2020)
- [OpenNMT: Neural Machine Translation Toolkit](https://mlsanity.com/paper/1805.11462/) (2018)
- [Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers](https://mlsanity.com/paper/2305.19474/) (2023)
- [On Language Models for Creoles](https://mlsanity.com/paper/2109.06074/) (2021)
- [Krey`ol-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages](https://mlsanity.com/paper/2405.05376/) (2024)
- [CreoleVal: Multilingual Multitask Benchmarks for Creoles](https://mlsanity.com/paper/2310.19567/) (2023)
- [Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgin](https://mlsanity.com/paper/2003.12660/) (2020)
- [Sheffield's Submission to the AmericasNLP Shared Task on Machine Translation into Indigenous Languages](https://mlsanity.com/paper/2306.09830/) (2023)
- [Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican](https://mlsanity.com/paper/2209.06295/) (2022)
- [Towards a general purpose machine translation system for Sranantongo](https://mlsanity.com/paper/2212.06383/) (2022)

## Built on by (most cited first, 2 of 2)

- [Context Volume Drives Performance: Tackling Domain Shift in Extremely Low-Resource Translation via RAG](https://mlsanity.com/paper/2601.09982/) (2026)
- [The University of Melbourne WMT 2026 CreoleMT Submission: A Domain-Balanced Approach to Low-Resource Pacific Creole Machine Translation](https://mlsanity.com/paper/2609.13615/) (2026)

## Abstract (arXiv)

We introduce a new dataset for machine translation of Guinea-Bissau Creole (Kiriol), comprising around 40 thousand parallel sentences to English and Portuguese. This dataset is made up of predominantly religious data (from the Bible and texts from the Jehovah's Witnesses), but also a small amount of general domain data (from a dictionary). This mirrors the typical resource availability of many low resource languages. We train a number of transformer-based models to investigate how to improve domain transfer from religious data to a more general domain. We find that adding even 300 sentences from the target domain when training substantially improves the translation performance, highlighting the importance and need for data collection for low-resource languages, even on a small-scale. We additionally find that Portuguese-to-Kiriol translation models perform better on average than other source and target language pairs, and investigate how this relates to the morphological complexity of the languages involved and the degree of lexical overlap between creoles and lexifiers. Overall, we hope our work will stimulate research into Kiriol and into how machine translation might better support creole languages in general.