@inproceedings{jourdain-etal-2026-chapsvision,
title = "{C}haps{V}ision ({CHV}) at {WMT} 2026 {C}reole{MT}: Shifting Creole {MT} from Train-Time to Test-Time Compute for {H}aitian {C}reole",
author = "Jourdain, Louis and
Pradelles, Aur{\'e}lie and
Semmar, Nasredine",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.187/",
pages = "2421--2447",
ISBN = "979-8-89176-474-3",
abstract = "We describe a training-free system for the WMT26 Creole MT shared task, targeting Haitian Creole in four directions (eng{\ensuremath{\leftrightarrow}}hat, fra{\ensuremath{\leftrightarrow}}hat). Rather than fine-tune, we wrap a sin- gle fixed 8B instruction model (qwen3-8b) in a language-agnostic harness that retrieves lexi- cal, parallel-data and grammatical evidence and injects it directly into the translation prompt. On FLORES+ devtest the harness lifts the base model by up to +14 chrF++ and clears the shared task{'}s primary baseline in all four di- rections, though a purpose-fine-tuned system (krey{\`o}l-MT) and frontier LLM still lead the mean. On the task{'}s blind test set that ordering reverses: we finish below both baselines, while the harness gain itself holds, staying large into Creole and small out of it. Two findings frame the paper. First, injected knowledge behaves as a reasoning equalizer: the same evidence that lifts a weak open model leaves a strong frontier model flat, so grounding substitutes for a capa- bility the model lacks rather than amplifying one it has. Second, how evidence is delivered dominates: forcing it into the prompt beats let- ting the model self-serve the same tools in an agentic (ReAct) loop, which costs about 6{\texttimes} the tokens."
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<abstract>We describe a training-free system for the WMT26 Creole MT shared task, targeting Haitian Creole in four directions (eng\ensuremathłeftrightarrowhat, fra\ensuremathłeftrightarrowhat). Rather than fine-tune, we wrap a sin- gle fixed 8B instruction model (qwen3-8b) in a language-agnostic harness that retrieves lexi- cal, parallel-data and grammatical evidence and injects it directly into the translation prompt. On FLORES+ devtest the harness lifts the base model by up to +14 chrF++ and clears the shared task’s primary baseline in all four di- rections, though a purpose-fine-tuned system (kreyòl-MT) and frontier LLM still lead the mean. On the task’s blind test set that ordering reverses: we finish below both baselines, while the harness gain itself holds, staying large into Creole and small out of it. Two findings frame the paper. First, injected knowledge behaves as a reasoning equalizer: the same evidence that lifts a weak open model leaves a strong frontier model flat, so grounding substitutes for a capa- bility the model lacks rather than amplifying one it has. Second, how evidence is delivered dominates: forcing it into the prompt beats let- ting the model self-serve the same tools in an agentic (ReAct) loop, which costs about 6× the tokens.</abstract>
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%0 Conference Proceedings
%T ChapsVision (CHV) at WMT 2026 CreoleMT: Shifting Creole MT from Train-Time to Test-Time Compute for Haitian Creole
%A Jourdain, Louis
%A Pradelles, Aurélie
%A Semmar, Nasredine
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F jourdain-etal-2026-chapsvision
%X We describe a training-free system for the WMT26 Creole MT shared task, targeting Haitian Creole in four directions (eng\ensuremathłeftrightarrowhat, fra\ensuremathłeftrightarrowhat). Rather than fine-tune, we wrap a sin- gle fixed 8B instruction model (qwen3-8b) in a language-agnostic harness that retrieves lexi- cal, parallel-data and grammatical evidence and injects it directly into the translation prompt. On FLORES+ devtest the harness lifts the base model by up to +14 chrF++ and clears the shared task’s primary baseline in all four di- rections, though a purpose-fine-tuned system (kreyòl-MT) and frontier LLM still lead the mean. On the task’s blind test set that ordering reverses: we finish below both baselines, while the harness gain itself holds, staying large into Creole and small out of it. Two findings frame the paper. First, injected knowledge behaves as a reasoning equalizer: the same evidence that lifts a weak open model leaves a strong frontier model flat, so grounding substitutes for a capa- bility the model lacks rather than amplifying one it has. Second, how evidence is delivered dominates: forcing it into the prompt beats let- ting the model self-serve the same tools in an agentic (ReAct) loop, which costs about 6× the tokens.
%U https://aclanthology.org/2026.wmt-main.187/
%P 2421-2447
Markdown (Informal)
[ChapsVision (CHV) at WMT 2026 CreoleMT: Shifting Creole MT from Train-Time to Test-Time Compute for Haitian Creole](https://aclanthology.org/2026.wmt-main.187/) (Jourdain et al., WMT 2026)
ACL