petermantis/gma

Code for ACL 2022 findings paper "Gaussian Multi-head Attention for Simultaneous Machine Translation"

Gaussian Multi-head Attention for Simultaneous Machine Translation

Source code for our ACL 2022 paper "Gaussian Multi-head Attention for Simultaneous Machine Translation" (PDF)

Our method is implemented based on the open-source toolkit Fairseq.

Core code of Gaussian Multi-head Attention is in fairseq/modules/gaussian_multihead_attention.py

Requirements and Installation

• Python version = 3.6

• PyTorch version = 1.7

• Install fairseq:

  git clone https://github.com/ictnlp/GMA.git
  cd GMA
  pip install --editable ./

Quick Start

Data Pre-processing

We use the data of IWSLT15 English-Vietnamese (download here) and WMT15 German-English (download here), and apply BPE with 32K merge operations on WMT15 German-English via subword_nmt/apply_bpe.py.

Then, we process the data into the fairseq format:

src = SOURCE_LANGUAGE
tgt = TARGET_LANGUAGE
train_data = PATH_TO_TRAIN_DATA
vaild_data = PATH_TO_VALID_DATA
test_data = PATH_TO_TEST_DATA
data=PATH_TO_DATA

fairseq-preprocess --source-lang ${src} --target-lang ${tgt} \
    --trainpref ${train_data} --validpref ${vaild_data} \
    --testpref ${test_data}\
    --destdir ${data} \
    --workers 20

Training

Train the GMA with the following command:

• delta is the relaxation offset to provide a controllable trade-off between translation quality and latency in practice, and we suggest set delta=1.0.

export CUDA_VISIBLE_DEVICES=0,1,2,3
data=PATH_TO_DATA
modelfile=PATH_TO_SAVE_MODEL
delta=SET_DELTA

python train.py --ddp-backend=no_c10d ${data} --arch transformer --share-all-embeddings \
 --optimizer adam \
 --adam-betas '(0.9, 0.98)' \
 --clip-norm 0.0 \
 --lr 5e-4 \
 --lr-scheduler inverse_sqrt \
 --warmup-init-lr 1e-07 \
 --warmup-updates 4000 \
 --dropout 0.3 \
 --criterion label_smoothed_cross_entropy \
 --label-smoothing 0.1 \
 --left-pad-source False \
 --delta ${delta} \
 --save-dir ${modelfile} \
 --max-tokens 4096 --update-freq 2

Inference

Evaluate the model with the following command:

export CUDA_VISIBLE_DEVICES=0
data=PATH_TO_DATA
modelfile=PATH_TO_SAVE_MODEL
ref_dir=PATH_TO_REFERENCE

# average last 5 checkpoints
python scripts/average_checkpoints.py --inputs ${modelfile} --num-update-checkpoints 5 --output ${modelfile}/average-model.pt 

# generate translation
python generate.py ${data} --path $modelfile/average-model.pt --batch-size 250 --beam 1 --left-pad-source False --remove-bpe > pred.out

grep ^H pred.out | cut -f1,3- | cut -c3- | sort -k1n | cut -f2- > pred.translation
multi-bleu.perl -lc ${ref_dir} < pred.translation

Our Results

The numerical results on IWSLT15 English-to-Vietnamese with Transformer-Small:

delta	CW	AP	AL	DAL	BLEU
0.9	1.20	0.65	3.05	4.08	27.95
1.0	1.27	0.68	4.01	4.77	28.20
2.0	1.49	0.74	5.47	6.37	28.44
2.2	1.60	0.77	6.04	6.96	28.56
2.5	1.74	0.78	6.55	7.55	28.72

The numerical results on WMT15 German-to-English with Transformer-Base:

delta	CW	AP	AL	DAL	BLEU
0.9	1.33	0.64	3.87	4.61	28.12
1.0	1.49	0.67	4.66	5.56	28.50
2.0	1.85	0.72	5.79	7.75	28.71
2.2	2.01	0.73	6.13	8.43	29.23
2.4	5.89	0.96	14.05	25.76	31.31

The numerical results on WMT15 German-to-English with Transformer-Big:

delta	CW	AP	AL	DAL	BLEU
1.0	1.54	0.68	4.60	5.89	30.20
2.0	1.98	0.74	6.34	8.18	30.64
2.2	2.13	0.75	6.86	8.91	31.33
2.4	2.28	0.76	7.28	9.59	31.62
2.5	3.10	0.88	12.06	20.43	31.91

Citation

In this repository is useful for you, please cite as:

@inproceedings{GMA,
	title = {Gaussian Multi-head Attention for Simultaneous Machine Translation},
	author = {Shaolei Zhang and Yang Feng},
	booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
	year = {2022},
}