ICLR 2020: Neural Machine Translation with universal Visual Representation
This implementation is based on fairseq. We take en2de NMT experiment for example.
*working in progress
- OS: macOS or Linux
- NVIDIA GPU and NCCL
- Pytorch
-
Download Multi30K dataset
git clone --recursive https://github.com/multi30k/dataset.git multi30k-dataset -
Visual Features
Pre-extracted visual features can be downloaded from Google Drive borrowed from the repo Multi30K.
The features are used in image embedding layer for indexing.
-
Data-Preprocessing
Segment both the NMT dataset and Multi30K dataset with the same BPE code file (built by the NMT dataset) using the tool subword-nmt.
Run
prepare-wmt-en2de.sh --icml17(for WMT'14 En-De), which is a modified version of prepare-wmt14en2de.sh
Before generating the lookup table, the following two files should be prepared:
- the segmented file for multi30k training set using the same BPE code with the NMT dataset (wmt14_en_de/bpe.multi30k.en)
Ensure the training sets of multi30k and NMT are segmented to subwords in the same manner (BPE code)
so the tokens in NMT dataset can be found in the lookup table built by multi30k sentence-image pairs.
- the source dict of WMT dataset （data/src_dict_wmt_en2de.txt）
*run the model using the following script, and the source dict file will be saved at the directory --save-dir*
**Note this is just for getting the dict file. The training will be interrupted without the lookup table, which is expected. Just start the training using the script after having the lookup table.
TEXT=wmt14_en_de
python preprocess.py --source-lang en --target-lang de --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test --destdir data-bin/wmt14_en2de --joined-dictionary --thresholdtgt 0 --thresholdsrc 0 --workers 20
DATA_DIR=data-bin/wmt14_en2de/
python train.py ${DATA_DIR} --task translation \
--arch transformer_wmt_en_de --share-all-embeddings --dropout 0.15 \
--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
--lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 8000 \
--lr 0.0007 --min-lr 1e-09 \
--criterion label_smoothed_cross_entropy --label-smoothing 0.1 --weight-decay 0.0 \
--max-tokens 4096\
--update-freq 1 --no-progress-bar --log-format json --log-interval 100 \
--save-interval-updates 1000 --keep-interval-updates 1000 --max-update 300000 --source-lang en --target-lang de \
--save-dir checkpoints/base-wmt-en2de \
--save_src_dict data/src_dict_wmt_en2de.txt \
--cap2image_file data/cap2image_en2de.pickle \
--image_embedding_file features_resnet50/train-resnet50-avgpool.npy \
--encoder-type TransformerAvgEncoder --L2norm true --image_emb_fix --total_num_img 5 --per_num_img 1 --find-unused-parameters --merge_option att-gate --gate_type neural-gate
Then we can get the lookup table for NMT model training.
bash sh_en2de_map.sh
python image_lookup.py \
--src_dict_dir data/src_dict_wmt_en2de.txt \
--src_en_dir wmt14_en_de/bpe.multi30k.en \
--image_dir multi30k-dataset/data/task1/image_splits/train.txt \
--cap2image_file data/cap2image_en2de.pickle
change the directory if needed:
parser.add_argument('--stopwords_dir', default="data/stopwords-en.txt", help='path of the stopwords-en.txt')
parser.add_argument('--src_dict_dir', default="data/src_dict_wmt_en2de.txt", help='path of the source dict of WMT dataset')
parser.add_argument('--src_en_dir', default="wmt14_en_de/bpe.multi30k.en", help='path of the segmented file for multi30k training set using the same bpe code with the nmt dataset (e.g., en2de)')
parser.add_argument('--image_dir', default="multi30k-dataset/data/task1/image_splits/train.txt", help='path of the image_splits of training set of multi30k')
The cap2image_file is the lookup table used for training NMT model.
parser.add_argument('--cap2image_file', default="data/cap2image_en2de.pickle", help='output file for (topic) word to image id lookup table')
TEXT=wmt14_en_de
python preprocess.py --source-lang en --target-lang de --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test --destdir data-bin/wmt14_en2de --joined-dictionary --thresholdtgt 0 --thresholdsrc 0 --workers 20
DATA_DIR=data-bin/wmt14_en2de/
python train.py ${DATA_DIR} --task translation \
--arch transformer_wmt_en_de --share-all-embeddings --dropout 0.15 \
--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
--lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 8000 \
--lr 0.0007 --min-lr 1e-09 \
--criterion label_smoothed_cross_entropy --label-smoothing 0.1 --weight-decay 0.0 \
--max-tokens 4096\
--update-freq 1 --no-progress-bar --log-format json --log-interval 100 \
--save-interval-updates 1000 --keep-interval-updates 1000 --max-update 300000 --source-lang en --target-lang de \
--save-dir checkpoints/base-wmt-en2de \
--save_src_dict data/src_dict_wmt_en2de.txt \
--cap2image_file data/cap2image_en2de.pickle \
--image_embedding_file features_resnet50/train-resnet50-avgpool.npy \
--encoder-type TransformerAvgEncoder --L2norm true --image_emb_fix --total_num_img 5 --per_num_img 1 --find-unused-parameters --merge_option att-gate --gate_type neural-gate
DATA_DIR=data-bin/wmt14_en2de
MODEL_DIR=checkpoints/base-wmt-en2de/checkpoint_best.pt
TEXT=wmt14_en_de
CUDA_VISIBLE_DEVICES=0 python interactive.py ${DATA_DIR} --input ${TEXT}/test.en \
--path ${MODEL_DIR} --beam 5 --remove-bpe --lenpen 0.6 \
--source-lang en --target-lang de --batch-size 64 --buffer-size 1000 > ./result/wmt14_ende_test.pred
A trained WMT'14 En2De model can be downloaded from here.
Please kindly cite this paper in your publications if it helps your research:
@inproceedings{zhang2020neural,
title={Neural Machine Translation with Universal Visual Representation},
author={Zhuosheng Zhang and Kehai Chen and Rui Wang and Masao Utiyama and Eiichiro Sumita and Zuchao Li and Hai Zhao},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Byl8hhNYPS}
}