Machine Translation Evaluation for Romansh

Code for the papers

• "Expanding the WMT24++ Benchmark with Rumantsch Grischun, Sursilvan, Sutsilvan, Surmiran, Puter, and Vallader"
• "Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties"

The benchmark data are located at https://huggingface.co/datasets/ZurichNLP/wmt24pp-rm and the human quality ratings are located at https://huggingface.co/datasets/ZurichNLP/romansh-mt-evaluation – this repo contains the code to reproduce the experiments in the papers.

Reproducing the results in "Expanding the WMT24++ Benchmark with Rumantsch Grischun, Sursilvan, Sutsilvan, Surmiran, Puter, and Vallader"

Installation

• pip install -r requirements.txt

Table 2 – Language classifier confusion matrix

• Download the language classifier (900MB size) from https://drive.switch.ch/index.php/s/5WM7aL2Nlo2wNDq
• python -m scripts.benchmark_paper.language_classifier_confusion_matrix /path/to/model.bin

Table 3 – Cross-variety ChrF

python -m scripts.benchmark_paper.cross_variety_scores

Table 4 – RM→DE: ChrF/xCOMET

We queried xCOMET-XL via a custom API – adapt the code to run the metric locally if needed.

python -m scripts.benchmark_paper.results_rm_to_de

Table 5 – DE→RM: ChrF

python -m scripts.benchmark_paper.results_de_to_rm

Figure 3 – Target variety adherence

python -m scripts.benchmark_paper.target_variety_adherence

Table 6 – Dataset statistics

python -m scripts.benchmark_paper.dataset_stats

Tables 7–12 – RM→DE: per-domain ChrF/xCOMET

We queried xCOMET-XL via a custom API – adapt the code to run the metric locally if needed.

python -m scripts.benchmark_paper.results_rm_to_de_detailed

Tables 13–18 – DE→RM: per-domain ChrF

python -m scripts.benchmark_paper.results_de_to_rm_detailed

xCOMET-XL is queried via the existing API client where applicable; run ChrF-only or adapt for local COMET if needed.

Reproducing the results in "Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties"

Installation

• pip install -r requirements.txt

Splitting WMT24++ into validation and test sets

• python -m scripts.mt_paper.split_wmt24pp_dataset
• This creates benchmarking/wmt24pp_split.json
• The first half of documents per domain is used as the validation split, and the remaining half is used as the test split

Table 1 – Automatic evaluation results

We queried xCOMET-XL via a custom API - adapt the code to run the metric locally if needed.

python -m scripts.mt_paper.results_automatic

Table 2 – Human evaluation results

python human_evaluation/scripts/create_results_table.py

Figure 2b – Target variety adherence

python -m scripts.mt_paper.target_variety_adherence_dual

Table 4 – Validation results for back-translation from Romansh

We queried xCOMET-XL via a custom API - adapt the code to run the metric locally if needed.

python -m scripts.mt_paper.results_validation_backtranslation

Appendix L – Detailed automatic evaluation results

We queried xCOMET-XL via a custom API - adapt the code to run the metric locally if needed.

python -m scripts.mt_paper.results_detailed_automatic

python -m scripts.mt_paper.target_variety_adherence_all_systems

Appendix M – Detailed human evaluation results

python human_evaluation/scripts/create_detailed_results_table.py

python human_evaluation/scripts/pairwise_system_comparison.py

Appendix N – Human evaluation statistics

python human_evaluation/scripts/create_human_evaluation_statistics_table.py

python human_evaluation/scripts/create_extended_inter_annotator_agreement.py

Collecting system translations

The collected system translations are stored in systems/.

Installation

• pip install -r requirements.txt
• PyTorch needed for MADLAD-400

MADLAD-400

for variety in rm-rumgr rm-sursilv rm-sutsilv rm-surmiran rm-puter rm-vallader; do
    python -m systems.madlad.run_translate "${variety}:de" google/madlad400-10b-mt <output_file>  # direct
    python -m systems.madlad.run_translate "${variety}:en:de" google/madlad400-10b-mt <output_file>  # pivoting via English
done

Translatur-ia

python -m systems.translaturia.collect_translations

Supertext

Translations were collected manually via the web interface of Supertext.

• Uploaded files are in systems/supertext/uploaded_files/
• Files returned by the web interface are in system_translations/wmt25_oldi/supertext/outputs/

LLMs

See instructions in ./wmt-collect-translations/README.md

Reproducing the benchmark data creation workflow

Creating the blank Excel files given to the translators

python -m creation_workflow.create_worksheets

Creating the benchmark dataset from the Excel files submitted by the translators

The submitted Excel files are in creation_workflow/completed_xlsx/

python -m creation_workflow.build_dataset

Tests

python -m unittest discover -s romansh_mt_eval/tests
python -m unittest discover -s romansh_mt_eval/human_evaluation/tests

Citation

@inproceedings{vamvas-et-al-2025-expanding,
    title = "Expanding the {WMT}24++ Benchmark with {R}umantsch {G}rischun, {S}ursilvan, {S}utsilvan, {S}urmiran, {P}uter, and {V}allader",
    author = "Vamvas, Jannis  and  P{\'e}rez Prat, Ignacio  and  Soliva, Not  and  Baltermia-Guetg, Sandra  and  Beeli, Andrina  and  Beeli, Simona  and  Capeder, Madlaina  and  Decurtins, Laura  and  Gregori, Gian Peder  and  Hobi, Flavia  and  Holderegger, Gabriela  and  Lazzarini, Arina  and  Lazzarini, Viviana  and  Rosselli, Walter  and  Vital, Bettina  and  Rutkiewicz, Anna  and  Sennrich, Rico",
    editor = "Haddow, Barry  and  Kocmi, Tom  and  Koehn, Philipp  and  Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.wmt-1.79/",
    pages = "1028--1047",
    ISBN = "979-8-89176-341-8",
}