Download model

Description	Dataset	Model
MASK-PREDICT	[WMT14 English-German]	download (.tar.bz2)
MASK-PREDICT	[WMT14 German-English]	download (.tar.bz2)
MASK-PREDICT	[WMT16 English-Romanian]	download (.tar.bz2)
MASK-PREDICT	[WMT16 Romanian-English]	download (.tar.bz2)
MASK-PREDICT	[WMT17 English-Chinese]	download (.tar.bz2)
MASK-PREDICT	[WMT17 Chinese-English]	download (.tar.bz2)

Preprocess

text=PATH_YOUR_DATA

output_dir=PATH_YOUR_OUTPUT

src=source_language

tgt=target_language

model_path=PATH_TO_MASKPREDICT_MODEL_DIR

python preprocess.py --source-lang ${src} --target-lang${tgt} --trainpref $text/train --validpref$text/valid --testpref $text/test --destdir${output_dir}/data-bin --workers 60 --srcdict {model_path}/maskPredict_{src}${tgt}/dict.${src}.txt --tgtdict {model_path}/maskPredict_{src}${tgt}/dict.${tgt}.txt

Train

model_dir=PLACE_TO_SAVE_YOUR_MODEL

python train.py ${output_dir}/data-bin --arch bert_transformer_seq2seq --share-all-embeddings --criterion label_smoothed_length_cross_entropy --label-smoothing 0.1 --lr 5e-4 --warmup-init-lr 1e-7 --min-lr 1e-9 --lr-scheduler inverse_sqrt --warmup-updates 10000 --optimizer adam --adam-betas '(0.9, 0.999)' --adam-eps 1e-6 --task translation_self --max-tokens 8192 --weight-decay 0.01 --dropout 0.3 --encoder-layers 6 --encoder-embed-dim 512 --decoder-layers 6 --decoder-embed-dim 512 --fp16 --max-source-positions 10000 --max-target-positions 10000 --max-update 300000 --seed 0 --save-dir${model_dir}

Evaluation

python generate_cmlm.py ${output_dir}/data-bin --path${model_dir}/checkpoint_best_average.pt --task translation_self --remove-bpe --max-sentences 20 --decoding-iterations 10 --decoding-strategy mask_predict

License

MASK-PREDICT is CC-BY-NC 4.0. The license applies to the pre-trained models as well.

Citation

Please cite as:

@inproceedings{ghazvininejad2019MaskPredict,
  title = {Mask-Predict: Parallel Decoding of Conditional Masked Language Models},
  author = {Marjan Ghazvininejad, Omer Levy, Yinhan Liu, Luke Zettlemoyer},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
  year = {2019},
}