This code is for ACL 2021 paper G-Transformer for Document-level Machine Translation.
Python Version: Python3.6
Package Requirements: torch==1.4.0 tensorboardX numpy==1.19.0
Framework: Our model and experiments are built upon fairseq.
Before running the scripts, please install fairseq dependencies by:
pip install --editable .
Please also follow the readmes under folder raw_data and mbart.cc25 to download raw data and pretrained model.
- Prepare data:
mkdir exp_randinit
bash exp_gtrans/run-all.sh prepare-randinit exp_randinit
- Train model:
CUDA_VISIBLE_DEVICES=0,1,2,3 bash exp_gtrans/run-all.sh run-randinit train exp_randinit
- Evaluate model:
bash exp_gtrans/run-all.sh run-randinit test exp_randinit
- Prepare data:
mkdir exp_finetune
bash exp_gtrans/run-all.sh prepare-finetune exp_finetune
- Train model:
CUDA_VISIBLE_DEVICES=0,1,2,3 bash exp_gtrans/run-all.sh run-finetune train exp_finetune
- Evaluate model:
bash exp_gtrans/run-all.sh run-finetune test exp_finetune
- Prepare data:
mkdir exp_mbart
bash exp_gtrans/run-all.sh prepare-mbart exp_mbart
- Train model:
CUDA_VISIBLE_DEVICES=0,1,2,3 bash exp_gtrans/run-all.sh run-mbart train exp_mbart
- Evaluate model:
bash exp_gtrans/run-all.sh run-mbart test exp_mbart