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GEMBA-MQM and GEMBA-DA

Setup

Install required packages with python >= 3.8

pip install -r requirements.txt

Set up secrets either for Azure API or OpenAI API:

export OPENAI_AZURE_ENDPOINT=
export OPENAI_AZURE_KEY=

or

export OPENAI_API_KEY=

Scoring with GEMBA

It assumes two files with the same number of lines. It prints the score for each line pair:

python main.py --source=source.txt --hypothesis=hypothesis.txt --source_lang=English --target_lang=Czech --method="GEMBA-MQM" --model="gpt-4"

The main recommended methods: GEMBA-MQM and GEMBA-DA with the model gpt-4.

Collecting and evaluating experiments for GEMBA-DA

Get mt-metric-eval and download resources:

git clone https://github.com/google-research/mt-metrics-eval.git
cd mt-metrics-eval
pip install .
alias mtme='python3 -m mt_metrics_eval.mtme'
mtme --download
cd ..
mv ~/.mt-metrics-eval/mt-metrics-eval-v2 mt-metrics-eval-v2

Collect data and run the scorer

python gemba_da.py 

export PYTHONPATH=mt-metrics-eval:$PYTHONPATH
python evaluate.py

License

GEMBA code and data are released under the CC BY-SA 4.0 license.

Paper

You can read more about GEMBA-DA in our arXiv paper or GEMBA-MQM in our arXiv paper.

How to Cite

GEMBA-MQM

@inproceedings{kocmi-federmann-2023-gemba-mqm,
    title = {GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4},
    author = {Kocmi, Tom  and Federmann, Christian},
    booktitle = "Proceedings of the Eighth Conference on Machine Translation",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
}

GEMBA-DA

@inproceedings{kocmi-federmann-2023-large,
    title = "Large Language Models Are State-of-the-Art Evaluators of Translation Quality",
    author = "Kocmi, Tom and Federmann, Christian",
    booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
    month = jun,
    year = "2023",
    address = "Tampere, Finland",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2023.eamt-1.19",
    pages = "193--203",
}