# Krey`ol-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages

arXiv: https://arxiv.org/abs/2405.05376 · HTML: https://mlsanity.com/paper/2405.05376/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 113 references that are in the map.
- First posted on arXiv 8 May 2024.

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

- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges](https://mlsanity.com/paper/1907.05019/) (2019)
- [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://mlsanity.com/paper/2309.04662/) (2023)
- [CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://mlsanity.com/paper/1911.04944/) (2019)
- [Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics](https://mlsanity.com/paper/2006.06264/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs](https://mlsanity.com/paper/1911.06154/) (2019)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [QED: A Framework and Dataset for Explanations in Question Answering](https://mlsanity.com/paper/2009.06354/) (2020)
- [Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems](https://mlsanity.com/paper/1912.09257/) (2019)
- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies](https://mlsanity.com/paper/1905.05475/) (2019)
- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation](https://mlsanity.com/paper/2212.10551/) (2022)
- [XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment](https://mlsanity.com/paper/2104.08597/) (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)
- [JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset](https://mlsanity.com/paper/2212.03419/) (2022)
- [Universal Dependencies Parsing for Colloquial Singaporean English](https://mlsanity.com/paper/1705.06463/) (2017)
- [CreoleVal: Multilingual Multitask Benchmarks for Creoles](https://mlsanity.com/paper/2310.19567/) (2023)
- [When Is TTS Augmentation Through a Pivot Language Useful?](https://mlsanity.com/paper/2207.09889/) (2022)
- [YANMTT: Yet Another Neural Machine Translation Toolkit](https://mlsanity.com/paper/2108.11126/) (2021)
- [BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation](https://mlsanity.com/paper/2111.06787/) (2021)
- [Data-adaptive Transfer Learning for Translation: A Case Study in Haitian and Jamaican](https://mlsanity.com/paper/2209.06295/) (2022)

## Built on by (most cited first, 10 of 10)

- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [LAG-MMLU: Benchmarking Frontier LLM Understanding in Latvian and Giriama](https://mlsanity.com/paper/2503.11911/) (2025)
- [Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creole](https://mlsanity.com/paper/2504.02674/) (2025)
- [Moly'e: A Corpus-based Approach to Language Contact in Colonial France](https://mlsanity.com/paper/2408.04554/) (2024)
- [DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Models](https://mlsanity.com/paper/2501.16581/) (2025)
- [Kr'eyoLID From Language Identification Towards Language Mining](https://mlsanity.com/paper/2503.06547/) (2025)
- [Limited-Resource Adapters Are Regularizers, Not Linguists](https://mlsanity.com/paper/2505.24525/) (2025)
- [The Rise of AfricaNLP: A Survey of Contributions, Contributors, Community Impact, and Bibliometric Analysis](https://mlsanity.com/paper/2509.25477/) (2025)
- [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)
- [Evaluating Cultural Awareness of LLMs for Haitian Creole](https://mlsanity.com/paper/2609.31506/) (2026)

## Abstract (arXiv)

A majority of language technologies are tailored for a small number of high-resource languages, while relatively many low-resource languages are neglected. One such group, Creole languages, have long been marginalized in academic study, though their speakers could benefit from machine translation (MT). These languages are predominantly used in much of Latin America, Africa and the Caribbean. We present the largest cumulative dataset to date for Creole language MT, including 14.5M unique Creole sentences with parallel translations -- 11.6M of which we release publicly, and the largest bitexts gathered to date for 41 languages -- the first ever for 21. In addition, we provide MT models supporting all 41 Creole languages in 172 translation directions. Given our diverse dataset, we produce a model for Creole language MT exposed to more genre diversity than ever before, which outperforms a genre-specific Creole MT model on its own benchmark for 26 of 34 translation directions.