nlp2ct/unite

UniTE

This is the repository of UniTE: Unified Translation Evaluation, which is build on COMET. Datasets can be found at COMET repository.

Training or Inference

The main repository is for inference. To train your own UniTE model, you can switch to training branch.

Usage

modelscope (recommended)

Recently we've released the related checkpoints on modelscope. We helped the development of the modelscope toolkit to support the usage of UniTE models. You can refer to the model cards of UniTE-UP and UniTE-MUP for more details.

To use those models on modelscope, first you need to install the modelscope repository (if you know Chinese well, you can follow this link for more details):

conda create -n modelscope python=3.9
conda activate modelscope

pip install "modelscope[nlp]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Then, you can refer to the given example codes on those two model cards.

Github

1. Download this repository via git;
2. Download the checkpoints from google drive;
3. Extract the zip file, and you can get checkpoints ('ckpt' as suffix) and corresponding setting files ('yaml' files, totally three);
4. Run score.py to score the files you want:

Source-Only
python score.py -s src.txt -r ref.txt -t trans.txt --model model.ckpt --to_json results.src.json --hparams_file_path hparams.src.yaml

Reference-Only
python score.py -s src.txt -r ref.txt -t trans.txt --model model.ckpt --to_json results.ref.json --hparams_file_path hparams.ref.yaml

Source-Reference-Combined
python score.py -s src.txt -r ref.txt -t trans.txt --model model.ckpt --to_json results.src_ref.json --hparams_file_path hparams.src_ref.yaml

where:

• src.txt stores the source inputs
• ref.txt stores the target reference
• trans.txt stores the translation outputs (can also be named as candidates or hypotheses)
• model.ckpt is the path of model checkpoint
• results.***.json is the output path of json-formatted scores
• hparams.***.yaml is the path of setting file

Citation

Please cite our paper if you find useful:

@inproceedings{wan2021robleurt,
    title = "{{RoBLEURT Submission for WMT2021 Metrics Task}}",
    author = "Wan, Yu  and
      Liu, Dayiheng  and
      Yang, Baosong  and
      Bi, Tianchi  and
      Zhang, Haibo  and
      Chen, Boxing  and
      Luo, Weihua  and
      Wong, Derek F.  and
      Chao, Lidia S.",
    booktitle = "Proceedings of the Sixth Conference on Machine Translation (WMT)",
    year = "2021",
}

@inproceedings{wan2022unite,
    title = "{{UniTE: Unified Translation Evaluation}}",
    author = "Wan, Yu  and
      Liu, Dayiheng  and
      Yang, Baosong  and
      Zhang, Haibo  and
      Chen, Boxing  and
      Wong, Derek F.  and
      Chao, Lidia S.",
    booktitle = "Annual Meeting of the Association for Computational Linguistics (ACL)",
    year = "2022",
}

@inproceedings{wan2022alibaba,
    title = "{{Alibaba-Translate China’s Submission for WMT 2022 Metrics Shared Task}}",
    author = "Wan, Yu  and
      Bao, Keqin  and
      Liu, Dayiheng  and
      Yang, Baosong  and
      Wong, Derek F.  and
      Chao, Lidia S.  and
      Lei, Wenqiang  and
      Xie, Jun",
    booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
    year = "2022",
}

@inproceedings{bao2022alibaba,
    title = "{{Alibaba-Translate China’s Submission for WMT 2022 Quality Estimation Shared Task}}",
    author = "Bao, Keqin  and
      Wan, Yu  and
      Liu, Dayiheng  and
      Yang, Baosong  and
      Lei, Wenqiang  and
      He, Xiangnan  and
      Wong, Derek F. and
      Xie, Jun",
    booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
    year = "2022",
}