yonebayashi / thesis-hien-nguyen Goto Github PK

To prepare the data for training, start with the following command:

bash prepare-iwslt15.sh

This will download the data from IWSLT'15 English-Vietnamese dataset, clean and tokenize it using Moses scripts, and finally apply BPE (Sennrich et al.) on the data.

This script sets the number of BPE tokens to 4000 and applies BPE dropout with p = 0.1 by default. To disable BPE dropout, remove the optional argument --dropout 0.1 in the following line:

python $BPEROOT/apply_bpe.py -c $BPE_CODE --dropout 0.1 < $tmp/train.$L > $tmp/bpe.train.$L

To binarize the data, run:

TEXT=iwslt15.tokenized.en-vi

fairseq-preprocess   --source-lang en --target-lang vi --trainpref $TEXT/train --validpref $TEXT/valid  --testpref $TEXT/test --destdir data-bin/iwslt15_en_vi_bpe4k --workers 20

To start the training, run:

CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt15_en_vi_bpe4k/ --lr 0.0001 --optimizer adam --clip-norm 0.0 --dropout 0.2 --max-tokens 4000 --batch-size 32 --arch transformer --update-freq 16 --save-dir checkpoints --no-epoch-checkpoints

All model checkpoints are saved in ./checkpoints. Since --no-epoch-checkpoints is set, only the last checkpoint and the best checkpoint will be saved.

To generate English to Vietnamese translations, run:

fairseq-generate data-bin/iwslt15_en_vi_bpe4k/  --source-lang en --target-lang vi --path checkpoints/checkpoint_best.pt --batch-size 8 --beam 4 --remove-bpe`

Translations are generated from the best model checkpoint, using beam search with default beam width 4. remove-bpe is set to remove BPE delimiters for easier reading.

Experimental results can be found here.

Thesis: https://www.overleaf.com/read/xgftpjdvccvb