AfroMT

Code for the EMNLP 2021 Paper AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages.

@inproceedings{reid21afromt,
    title = {Afro{MT}: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages},
    author = {Machel Reid and Junjie Hu and Graham Neubig and Yutaka Matsuo},
    booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},
    address = {Punta Cana, Dominican Republic},
    month = {November},
    url = {https://arxiv.org/abs/2109.04715},
    year = {2021}
}

Clone the repo

git clone git@github.com:machelreid/afromt

Data

Please use gdown to download the data

cd afromt
gdown --id 1Qj3IXQ9kusaeHtGYRVPzMnGm_dQvvfJP
tar -xf afromt.tar.xz

Installation

Run the following commands

cd training
# install apex for fp16 for faster training
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
cd ../
# install fairseq
pip install -e .
cd ../

Model usage

Download the model as follows:

gdown --id 1A9kbWHnMrjFwgq9x8rpnU4aATx_Y4b3b
tar -xf afrobart.tar.xz # and you will get afrobart.pt which is the model file

Preprocessing

First be sure to install sentencepiece

TGT_LANG=#whatever target language you choose
cd afromt # the data folder
cd en-$TGT_LANG
bash ../../preprocess.sh $TGT_LANG ../../
cd ../../

to preprocess $TGT_LANG (e.g. zu for Zulu, or xh for Xhosa). This command will produce a data-bin-afromt folder in the folder for the en-$TGT_LANG language pair.

Training

More details within the script itself if you want to tweak training arguments!

bash train_afrobart.sh afromt/en-$TGT_LANG/data-bin-afromt afromt/en-$TGT_LANG/model_output $TGT_LANG

and you should see your model training!

Evaluation

Once training is over, you can generate from the model as follows:

fairseq-generate afromt/en-$TGT_LANG/data-bin-afromt --gen-subset test --path afromt/en-$TGT_LANG/model_outputs/checkpoints/checkpoint_best.pt  --beam 5 --batch-size 300 --remove-bpe sentencepiece --truncate-source --task translation_from_xbart -s en -t $TGT_LANG --lenpen $LENPEN > generation.log 

(where $TGT_LANG and $LENPEN are variables controlling target language and length penalty respectively)

and generate BLEU scores as follows:

bash eval_bleu_chrf.sh generation.log

Other tidbits

• The bilingual dictionaries we extracted are in the dictionaries/ folder (note that these are automatically extracted with a word aligned so they are not gold standard)