Code for the research paper "Trained MT Metrics Learn to Cope with Machine-translated References"

Installation

• Install PyTorch
• cd .. && pip install -r requirements.txt
• pip install git+https://github.com/google-research/mt-metrics-eval

mt-metrics-eval

• python -m mt_metrics_eval.mtme --download (Puts ~1G of data into $HOME/.mt-metrics-eval)

Downloading the pre-trained model

• wget http://data.statmt.org/prism/m39v1.tar
• tar xf m39v1.tar
• mkdir models
• mv m39v1 models

Preparing the data

Downloading MQM data

See scripts/download_data.sh

Extracting relative rankings

• mkdir data/wmt_rr
• python scripts/convert_mqm_to_relative_ranking_data.py

Concatenating the language pairs and creating a train–valid split

See scripts/create_data_split.sh

Preprocessing data for Prism fine-tuning with fairseq

• mkdir data/prism_finetuning_data
• python scripts/prepare_prism_finetuning_data.py (might take a while)

Fine-tuning

• bash scripts/finetune_main.sh

Metric usage

Please refer to the reference implementation of Prism (https://github.com/thompsonb/prism) for instructions on using the metric

Meta-evaluation

• pip install -r requirements-eval.txt
• python scripts/run_meta_evaluation.py

Post-editese experiments

• python post_editese/scripts/run_.py

Citation

Please cite this work as:

@misc{vamvasetal2023trainedmetrics,
      title={Trained MT Metrics Learn to Cope with Machine-translated References},
      author={Vamvas, Jannis and Domhan, Tobias and Trenous, Sony and Sennrich, Rico and Hasler, Eva},
      booktitle={Proceedings of the Eighth Conference on Machine Translation (WMT)},
      year={2023}
}

Security

See CONTRIBUTING for more information.

License

This library is licensed under the CC-BY-NC-4.0 License.