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signwriting-translation

This repo contains code and documentation for training bilingual and multilingual translation models between spoken languages and signed languages in SignWriting, a writting notation system (not a glossing system!) of signed languages.

We also provide an API server for inferring based on the best trained models. A live demo translator from spoken languages to signed languages based on this is available on sign.mt.

sign.mt API Upgrade (July 2024)

sign.mt no longer relies on the API provided by this work to translate from text to SignWriting.

It now uses a model trained here (which supports more languages and better fingerspelling):

https://github.com/sign-language-processing/signwriting-translation

and stored here:

https://huggingface.co/sign/sockeye-text-to-factored-signwriting/tree/main

The old API and models in this work are still online.

SignBank+ (December 2023)

We rerun our models on the new datasets introduced in the follow-up work SignBank+, please see details in:

https://github.com/J22Melody/signwriting-translation/tree/main/scripts_new

This gives the new state-of-the-art SignWriting-to-text translation performance.

Citation

Please cite our paper as follows:

@inproceedings{jiang-etal-2023-machine,
    title = "Machine Translation between Spoken Languages and Signed Languages Represented in {S}ign{W}riting",
    author = {Jiang, Zifan  and
      Moryossef, Amit  and
      M{\"u}ller, Mathias  and
      Ebling, Sarah},
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-eacl.127",
    pages = "1661--1679",
    abstract = "This paper presents work on novel machine translation (MT) systems between spoken and signed languages, where signed languages are represented in SignWriting, a sign language writing system. Our work seeks to address the lack of out-of-the-box support for signed languages in current MT systems and is based on the SignBank dataset, which contains pairs of spoken language text and SignWriting content. We introduce novel methods to parse, factorize, decode, and evaluate SignWriting, leveraging ideas from neural factored MT. In a bilingual setup{---}translating from American Sign Language to (American) English{---}our method achieves over 30 BLEU, while in two multilingual setups{---}translating in both directions between spoken languages and signed languages{---}we achieve over 20 BLEU. We find that common MT techniques used to improve spoken language translation similarly affect the performance of sign language translation. These findings validate our use of an intermediate text representation for signed languages to include them in natural language processing research.",
}

Environment

We have the following main dependencies:

  • We use Python 3.8.11 (a virtual Python environment is recommended).
  • We use sign-language-datasets as our training data source.
  • We use SentencePiece for tokenization.
  • We use joeynmt for training models translating from signed languages to spoken languages (SIGN to SPOKEN).
  • We use Sockeye for training models translating from spoken languages to signed languages (SPOKEN to SIGN), which in turn depends on MXNet.

Install common dependencies:

pip install -r requirements.txt

sudo apt-get install sentencepiece

(or on Mac brew apt-get install sentencepiece)

For SIGN to SPOKEN, install joeynmt (a custom version supporting source factors):

pip install git+ssh://git@github.com/J22Melody/joeynmt.git@factors_complete

or

pip install <path_to_local_joeynmt>

For SPOKEN to SIGN, install MXNet and Sockeye:

pip install --pre -f https://dist.mxnet.io/python 'mxnet==2.0.0b20220206'

pip install sockeye==3.0.13

Note joeynmt and Sockeye require different versions of the package sacreBLEU, thus results in version conflict if both installed.

For training, we recommend two separate environments for the two different settings. For inferring (using the API server), this conflict can be safely ignored because sacreBLEU is not used there.

Data Analysis

python analyze_dataset.py

which results in dataset_stats.txt.

For a more visual overview in a Colab Notebook.

Model Training

40K SIGN to SPOKEN (Bilingual)

Translate from Formal SignWriting (FSW) of American Sign Language to American English.

Prepare data in ./data_bilingual/ directory:

python ./scripts/fetch_data_bilingual.py

sh ./scripts/preprocess_bilingual.sh

Train the start-of-art model baseline_transformer_spm_factor_sign+ from scratch:

python -m joeynmt train ./configs/baseline_transformer_spm_factor_sign+.yaml

Test, postprocess and evaluate it:

sh ./scripts/evaluate.sh baseline_transformer_spm_factor_sign+

See full results of all experiments here.

100K SIGN to SPOKEN (Multilingual)

Extend the first from a bilingual setting to a multilingual setting, translate from FSW of 4 signed languages to 4 corresponding spoken languages.

Prepare data in ./data/ directory:

python ./scripts/fetch_data.py

sh ./scripts/preprocess.sh

Train the start-of-art model baseline_multilingual from scratch:

python -m joeynmt train ./configs/baseline_multilingual.yaml

Test, postprocess and evaluate it:

sh ./scripts/evaluate_multilingual.sh baseline_multilingual

We also have an additional multilingual model that contains 21 language pairs, see branch multilingual_plus (not well documented).

See full results of both experiments here.

100K SPOKEN to SIGN (Multilingual)

Translate the reverse direction, from 4 spoken languages to FSW of 4 corresponding signed languages.

Prepare data in ./data_reverse/ directory:

python ./scripts/fetch_data.py

sh ./scripts/preprocess_reverse.sh

sh ./scripts/sockeye_prepare_factor.sh

Train the start-of-art model sockeye_spoken2symbol_factor_0.1 from scratch:

sh ./scripts/sockeye_train_factor.sh

Test, postprocess and evaluate it:

sh ./scripts/sockeye_translate_factor.sh sockeye_spoken2symbol_factor_0.1

sh ./scripts/sockeye_evaluate_factor.sh sockeye_spoken2symbol_factor_0.1

sh ./scripts/sockeye_evaluate_factor_multilingual.sh sockeye_spoken2symbol_factor_0.1

See full results of all experiments here.

API Server

First make sure you have the model checkpoint files in place, which are not part of this repo.

Run Flask locally for debugging:

python app.py

Run with Gunicorn for deployment:

gunicorn -w 4 -b 0.0.0.0:3030 app:app

Example Supervisor config file (/etc/supervisor/conf.d/gunicorn.conf):

[program:gunicorn]
user=xxx
directory=/home/xxx/signwriting-translation
command=gunicorn -w 4 -b 0.0.0.0:3030 app:app

autostart=true
autorestart=true
stdout_logfile=/home/xxx/log/gunicorn.log
stderr_logfile=/home/xxx/log/gunicorn.err.log

See API_spec.md for API specifications.