K oz K reol MRU WMT 2025 C reole MT System Description: Koz Kreol: Multi-Stage Training for E nglish–Mauritian Creole MT (2025)

Abstract: Mauritian Creole (Kreol Morisyen), spoken by approximately 1.5 million people worldwide, faces significant challenges in digital language technology due to limited computational resources. This paper presents “Koz Kreol”, a comprehensive approach to English–Mauritian Creole machine translation using a three-stage training methodology: monolingual pretraining, parallel data training, and LoRA fine-tuning. We achieve state-of-the-art results with a 28.82 BLEU score for EN→MFE translation, representing a 74% improvement over ChatGPT-4o. Our work addresses critical data scarcity through the use of existing datasets, synthetic data generation, and community-sourced translations. The methodology provides a replicable framework for other low-resource Creole languages while supporting digital inclusion and cultural preservation for the Mauritian community. This paper consists of both a systems and data subtask submission as part of a Creole MT Shared Task.

## introduction mauritian creole 1 is spoken by individuals from mauritius, rodrigues, agalega and the chagos archipelago. over the course of its history, mauritius was visited by the arabs, colonized by the dutch, french and the british. originally, it is a language made of french and afro-malagasy languages which was used as a means of communication between slaves and their french masters @xcite . over time, as the english rule began and indentured labourers arrived from india, more words infiltrated the existing mauritian creole lexicon. with this deep diversity of linguistic families, mauritian creole has words that can etymologically be traced back to france, england, madagascar and both north and south india @xcite . a defining characteristic of creole lan-1 also referred to as "kreol morisyen" guages is their dynamic lexicon, which often exhibits clear phonetic and semantic shifts from their source languages @xcite . for example, in mauritian creole, @xmath0. however, the exact etymology of the word "kalamindas" is unknown. this multilingual substrate influence is characteristic of creole formation processes, where multiple source languages contribute to the emerging creole's lexicon and structure @xcite . mauritian creole is considered to be a lowresource language since it lacks digital computational resources for language technology applications @xcite . despite having a vibrant community of speakers, mauritian creole, like many creole languages, faces social stigmatization and is often perceived as linguistically inferior or underdeveloped compared to its lexifier languages. @xcite .throughout the years, several efforts have been made by the government of mauritius to enforce mauritian creole as a language rather than a dialect, part of a broader movement for creole language recognition and standardization @xcite . mauritian creole has become part of the school curriculum when teaching languages at an early age. however since most mauritian creole media are through traditional sources like newspapers or magazines, few digital resources exist. creoles are generally under represented in language research since they are generally low resource languages whose datasets are seldom publicly available. this digital divide creates significant barriers for the mauritian community's participation in the modern digital economy and limits access to language technologies that could support cultural preservation and digital inclusion. @xcite this work addresses these challenges by developing "koz kreol," a comprehensive machine translation system for english-mauritian creole translation. we present a three-stage training methodology combining monolingual pretraining, parallel data training, and low rank adaptation (lora) fine-tuning that achieves state-of-the-art performance on this language pair. our approach strategically combines existing datasets from previous research efforts with high-quality communitysourced translations and synthetic data generation. the resulting system not only advances the state of machine translation for mauritian creole but also provides a replicable framework for developing mt systems for other low-resource creole languages, contributing to broader efforts in digital language inclusion and cultural preservation. this paper consists of both a systems and data subtask submission as part of a creole mt shared task @xcite . ## related work low resource machine translation (lrmt) has evolved from early transfer learning approaches @xcite and backtranslation techniques @xcite to sophisticated multilingual pre-trained models like mbart @xcite , which achieved up to 12 bleu foot_0 @xcite points improvement for low-resource pairs foot_1 . the paradigm for low-resource languages was further established by mbart-50 @xcite , which scaled multilingual pre-training to 50 languages and became the standard approach for many low-resource translation tasks. early work by @xcite established benchmarks for learning translation from minimal linguistic resources, demonstrating how grammar books alone can provide sufficient structural information for basic translation capabilities in truly low-resource scenarios. creole languages present distinctive challenges beyond typical low-resource scenarios due to their genealogical complexity, orthographic variability, and historical stigmatization, requiring multisource transfer learning rather than conventional single-source approaches. recent creole mt research has made substantial progress across multiple fronts. dabre and sukhoo (2022) established foundational baselines with kreolmorisienmt, creating the first comprehensive parallel corpus for mauritian creole with 21,810 sentence pairs and demonstrating effective transfer learning from pre-trained multilingual models. @xcite dramatically scaled creole coverage with kreyòl-mt, presenting 14.5 million unique creole sentences across 41 languages supporting 172 translation directions. @xcite introduced cre-oleval, the first comprehensive benchmark spanning 8 nlp tasks across 28 creole languages. @xcite explored parameter-efficient approaches through adapter architectures for crosslingual transfer, while @xcite demonstrated that strategic fine-tuning of large pre-trained models with small amounts of high-quality data can achieve significant improvements. ## results for the sake of our experiment, since mauritian creole data is scarce, we are training on "devtest" and we use the lalit test set for evaluation. ## conclusions in this paper, we present a new state-of-the-art model for english-mauritian creole translation, along with several novel datasets used for training and evaluation. these include: (1) the flores-200 dev and devtest sets, (2) synthetically generated data, (3) a test set from the lalit newspaper, and (4) community-sourced parallel bitext. this work provides a strong baseline for future model development, which can be improved by collecting more high-quality parallel data. our results with limited data suggest that incremental augmentation will boost performance, supporting sustainable mauritian creole mt development. additionally, our model can generate high-quality synthetic translations, enabling continual learning through iterative data generation and refinement. several promising directions emerge for future research. the inclusion of french parallel bitext represents a particularly valuable avenue, given mauritian creole's french lexifier heritage and the abundance of high-quality french-english parallel corpora that could enhance transfer learning effectiveness @xcite . incorporating pivot languages; intermediate languages that share linguistic features with both english and mauritian creole, could provide additional pathways for cross-lingual knowledge transfer and improved translation quality. finally, the systematic generation of synthetic training data through back-translation, paraphrasing, and multilingual data augmentation techniques offers scalable approaches to address the persistent data scarcity challenges that characterize creole language processing. these future developments, building upon the foundation established in this work, promise to advance mauritian creole machine translation toward broader practical applicability and community benefit.