Hidden Markov Transformer for Simultaneous Machine Translation

Shaolei Zhang, Yang Feng

Source code for our ICLR 2023 spotlight paper "Hidden Markov Transformer for Simultaneous Machine Translation"

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

Requirements and Installation

• Python version = 3.6

• PyTorch version = 1.7

• Install fairseq:

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

Quick Start

Since the training time of HMT on WMT15 De-En is slightly longer, we provide the trained HMT model of WMT15 De-En for reproduction. The corresponding Fairseq binary data of WMT15 De-En can be download here. You can go directly to the inference part to test HMT, with the provided checkpoints and binary data.

L	K	Download Link
-1	4	[百度网盘 Baidu Skydisk]
2	4	[百度网盘 Baidu Skydisk]
3	6	[百度网盘 Baidu Skydisk]
5	6	[百度网盘 Baidu Skydisk]
7	6	[百度网盘 Baidu Skydisk]
9	8	[百度网盘 Baidu Skydisk]
11	8	[百度网盘 Baidu Skydisk]

Data Pre-processing

We use the data of IWSLT15 English-Vietnamese (download here) and WMT15 German-English (download here).

For WMT15 German-English, we tokenize the corpus via mosesdecoder/scripts/tokenizer/normalize-punctuation.perl and apply BPE with 32K merge operations via subword_nmt/apply_bpe.py. Follow preprocess scripts to perform tokenization and BPE.

Then, we process the data into the fairseq format, adding --joined-dictionary for WMT15 German-English:

SRC=source_language
TGT=target_language
TRAIN=path_to_train_data
VAIILD=path_to_vaild_data
TEST=path_to_test_data
DATAFILE=dir_to_data

# add --joined-dictionary for WMT15 German-English
fairseq-preprocess --source-lang ${SRC} --target-lang ${TGT} \
    --trainpref ${TRAIN} --validpref ${VAIILD} \
    --testpref ${TEST}\
    --destdir ${DATAFILE} \
    --workers 20

Training

Train the HMT with the following command:

• --first-read: low boundary $L$ in hidden Markov Transformer
• --cands-per-token: state number $K$ in hidden Markov Transformer

export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

MODELFILE=dir_to_save_model
DATAFILE=dir_to_data

FIRST_READ=3
CANDS_PER_TOKEN=6

python train.py --ddp-backend=no_c10d ${DATAFILE} --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 \
 --encoder-attention-heads 8 \
 --decoder-attention-heads 8 \
 --criterion label_smoothed_cross_entropy \
 --label-smoothing 0.1 \
 --left-pad-source False \
 --save-dir ${MODELFILE} \
 --first-read ${FIRST_READ} \
 --cands-per-token ${CANDS_PER_TOKEN} \
 --max-tokens 4096 --update-freq 1 \
 --max-target-positions 200 \
 --skip-invalid-size-inputs-valid-test \
 --fp16 \
 --save-interval-updates 1000 \
 --keep-interval-updates 300 \
 --log-interval 10

Inference

Evaluate the model with the following command:

• Note that simultaneous machine translation require --batch-size=1with --sim-decoding.

export CUDA_VISIBLE_DEVICES=0
MODELFILE=dir_to_save_model
DATAFILE=dir_to_data
REF=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 ${DATAFILE} --path ${MODELFILE}/average-model.pt --batch-size 1 --beam 1 --left-pad-source False --fp16  --remove-bpe --sim-decoding > pred.out

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

• For decoding efficiency, we also provide a parallel generating version. The results are consistent with sim-decoding.

# generate translation
python generate.py ${data} --path $modelfile/average-model.pt --batch-size 250 --beam 1 --left-pad-source False --fp16  --remove-bpe > pred.out

Our Results

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

L	K	CW	AP	AL	DAL	BLEU
1	2	1.15	0.64	3.10	3.72	27.99
2	2	1.22	0.67	3.72	4.38	28.53
4	2	1.24	0.72	4.92	5.63	28.59
5	4	1.53	0.76	6.34	6.86	28.78
6	4	1.96	0.83	8.15	8.71	28.86
7	6	2.24	0.89	9.60	10.12	28.88

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

L	K	CW	AP	AL	DAL	BLEU
-1	4	1.58	0.52	0.27	2.41	22.52
2	4	1.78	0.59	2.20	4.53	27.60
3	6	2.06	0.64	3.46	6.38	29.29
5	6	1.85	0.69	4.74	6.95	30.29
7	6	2.03	0.74	6.43	8.35	30.90
9	8	2.48	0.79	8.37	10.09	31.45
11	8	3.02	0.83	10.06	11.57	31.61
13	8	3.73	0.86	11.80	13.08	31.71

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

L	K	CW	AP	AL	DAL	BLEU
-1	4	1.65	0.52	0.06	2.43	22.70
2	4	1.79	0.60	2.19	4.50	27.97
3	6	2.04	0.64	3.46	6.30	29.91
5	6	1.88	0.69	4.85	7.07	30.85
7	6	2.06	0.74	6.56	8.47	31.99
9	8	2.47	0.79	8.34	10.10	31.98
11	8	2.98	0.83	10.12	11.58	32.46
13	8	3.75	0.86	11.78	13.09	32.58

Citation

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

@inproceedings{zhang2023hidden,
    title={Hidden Markov Transformer for Simultaneous Machine Translation},
    author={Shaolei Zhang and Yang Feng},
    booktitle={International Conference on Learning Representations},
    year={2023},
    url={https://openreview.net/forum?id=9y0HFvaAYD6}
}