README.md

This repository presents UA-COMET – an extension of the COMET metric implemented by Unbabel.

It contains the code and data to reproduce the experiments in Uncertainty-Aware Machine Translation Evaluation.

Quick Installation

We recommend python 3.6 to run COMET.

Detailed usage examples and instructions can be found in the Full Documentation.

To develop locally:

git clone https://github.com/deep-spin/UA_COMET.git
pip install -r requirements.txt
pip install -e .

Scoring MT outputs:

Via Bash:

Examples from WMT20:

echo -e "Dem Feuer konnte Einhalt geboten werden\nSchulen und Kindergärten wurden eröffnet." >> src.de
echo -e "The fire could be stopped\nSchools and kindergartens were open" >> hyp.en
echo -e "They were able to control the fire.\nSchools and kindergartens opened" >> ref.en

comet score -s src.de -h hyp.en -r ref.en

You can export your results to a JSON file using the --to_json flag and select another model/metric with --model.

comet score -s src.de -h hyp.en -r ref.en --model wmt-large-hter-estimator --to_json segments.json

Via Python:

from comet.models import download_model
model = download_model("wmt-large-da-estimator-1719")
data = [
    {
        "src": "Dem Feuer konnte Einhalt geboten werden",
        "mt": "The fire could be stopped",
        "ref": "They were able to control the fire."
    },
    {
        "src": "Schulen und Kindergärten wurden eröffnet.",
        "mt": "Schools and kindergartens were open",
        "ref": "Schools and kindergartens opened"
    }
]
model.predict(data, cuda=True, show_progress=True)

Scoring MT outputs with MCD runs

To run COMET with multiple MCD runs:

 #!/bin/bash
 
GPU_N=3

SCORES=/path/to/your/output/folder
DATA=/path/to/your/data/folder

N=100
D=0.1
N_REFS=1

SRC=src.txt
MT=mt.txt
REF=ref.txt

MODEL=wmt-large-da-estimator-1719

echo Starting the process...

CUDA_VISIBLE_DEVICES=$GPU_N comet score \
  -s $DATA/sources/$SRC \
  -h $DATA/system-outputs/$MT \
  -r $DATA/references/$REF \
  --to_json $SCORES/filename.json \
  --n_refs $N_REFS \
  --n_dp_runs $N \
  --d_enc $D \
  --d_pool $D \
  --d_ff1 $D \
  --d_ff2 $D \
  --model $MODEL 

This will run the model with a set of hyperparameters defined above. Here is the description of the main scoring arguments:

-s: Source segments.
-h: MT outputs.
-r: Reference segments.
--to_json: Creates and exports model predictions to a JSON file.
--n_refs: Number of references used during inference. [default=1]
--n_dp_runs: Number of dropout runs at test time. [default=30]
--d_enc: Dropout value for the encoder. [default=0.1]
--d_pool: Dropout value for the layerwise pooling layer. [default=0.1]
--d_ff1: Dropout value for the 1st feed forward layer. [default=0.1]
--d_ff2: Dropout value for the 2nd feed forward layer. [default=0.1]
--model: Name of the pretrained model OR path to a model checkpoint.

To know more about the rest of the parameters and their default values, take a look at the comet/cli.py file.

How to Reproduce and Evaluate Experiments

The evaluation sub-folder contains the scripts and data necessary to reproduce the experiments presented in Uncertainty-Aware Machine Translation Evaluation and/or test new model outputs. See the README in that folder for more detailed instructions.

Model Zoo:

The COMET models used for uncertainty-aware MT evaluation experiments are:

• wmt-large-da-estimator-1719 for the WMT20 dataset (DA/MQM scores)
• wmt-large-hter-estimator for the QT21 dataset (HTER scores)

Available and compatible models are:

Model	Description
↑wmt-large-da-estimator-1719	RECOMMENDED: Estimator model build on top of XLM-R (large) trained on DA from WMT17, WMT18 and WMT19
↑wmt-base-da-estimator-1719	Estimator model build on top of XLM-R (base) trained on DA from WMT17, WMT18 and WMT19
↓wmt-large-hter-estimator	Estimator model build on top of XLM-R (large) trained to regress on HTER.
↓wmt-base-hter-estimator	Estimator model build on top of XLM-R (base) trained to regress on HTER.

Train your own Metric:

Instead of using pretrained models your can train your own COMET model with the following command:

comet train -f {config_file_path}.yaml

For more information check: COMET's documentation.

Alternatively, it is possible to train a different metric and compare performance using the scripts in the evaluation sub-folder. In this case, ensure the metric output files maintain the same structure as described in evaluation/data/README.md.

Publications

@inproceedings{rei-etal-2020-comet,
    title = "{COMET}: A Neural Framework for {MT} Evaluation",
    author = "Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
    pages = "2685--2702",
}

@inproceedings{rei-EtAl:2020:WMT,
  author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
  title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},
  booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
  month          = {November},
  year           = {2020},
  address        = {Online},
  publisher      = {Association for Computational Linguistics},
  pages     = {909--918},
}

@inproceedings{stewart-etal-2020-comet,
    title = "{COMET} - Deploying a New State-of-the-art {MT} Evaluation Metric in Production",
    author = "Stewart, Craig  and
      Rei, Ricardo  and
      Farinha, Catarina  and
      Lavie, Alon",
    booktitle = "Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)",
    month = oct,
    year = "2020",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://www.aclweb.org/anthology/2020.amta-user.4",
    pages = "78--109",
}