# The University of Melbourne WMT 2026 CreoleMT Submission: A Domain-Balanced Approach to Low-Resource Pacific Creole Machine Translation

arXiv: https://arxiv.org/abs/2609.13615 · HTML: https://mlsanity.com/paper/2609.13615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 12 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://mlsanity.com/paper/2309.04662/) (2023)
- [WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2010.03093/) (2020)
- [A Survey of Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1806.00258/) (2018)
- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation](https://mlsanity.com/paper/2502.04314/) (2025)
- [Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1907.03060/) (2019)
- [Krey`ol-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages](https://mlsanity.com/paper/2405.05376/) (2024)
- [Ancestor-to-Creole Transfer is Not a Walk in the Park](https://mlsanity.com/paper/2206.04371/) (2022)
- [When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation Evaluation](https://mlsanity.com/paper/2601.20858/) (2026)
- [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)

## Abstract (arXiv)

For our submission to the WMT26 Creole Language Translation Shared Task, we focus on machine translation (MT) models for Pacific creoles: Tok Pisin, Bislama, and Solomon Pijin, with particular attention to broad domain performance. After pre-training on a large collection of domain-imbalanced data, we continue fine-tuning on a diverse mix of domain-balanced data. We rely on a number of data collection and preparation techniques, including LLM-assisted respelling and alignment, back-translation, and distillation from Gemini for domains originally not present in training data. Evaluated on Bouquet and a novel test set made of spoken language transcripts, our models beat open model baselines by 3+ chrF++ points in all directions with human-original references. Looking ahead, we plan to develop human-translated test sets for Solomon Pijin and Bislama, and to distil our best models into much smaller ones that retain broad domain coverage.