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Frozen Pretrained Transformers for Neural Sign Language Translation

This is the implementation for the paper Frozen Pretrained Transformers for Neural Sign Language Translation presented at AT4SSL 2021.

This code is based on the implementation of Sign Language Transformers: Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation, available here.

Requirements

Download the feature files using the data/download.sh script.
[Optional] Create a conda or python virtual environment.
Install required packages using the requirements.txt file.

pip install -r requirements.txt

Usage

Choose the configuration file that you want to reproduce and update the data.data_path and training.model_dir configuration entries to the path where your data resides (default: data/PHOENIX2014T) and the path where you want the experiment logs and checkpoints to be saved.

python -m signjoey train configs/$CONFIG.yaml

For the mBART-50 model, you will first need to tokenize the corpus using the mBART-50 tokenizer. You can use the tokenization/tokenize_mbart50.py script for this.

Citation

If you use this code in one of your projects, please cite

@InProceedings{De_Coster_2021_AT4SSL,
    author    = {De Coster, Mathieu and D'Oosterlinck, Karel and Pizurica, Marija and Rabaey, Paloma and Verlinden, Severine and Van Herreweghe, Mieke and Dambre, Joni},
    title     = {Frozen Pretrained Transformers for Neural Sign Language Translation},
    booktitle = {1st International Workshop on Automated Translation for Signed and Spoken Languages},
    month     = {August},
    year      = {2021},
}

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