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Integrating Vectorized Lexical Constraints for Neural Machine Translation

Prerequisites

This repository is based on Fairseq. Please see here for the environment configuration.

Usage

Suppose the local path to this repository is CODE_DIR.

Step 1: Data Preparation

We firstly use this script to extract the lexical constraints for the training, validation and test sets. After that, we prepend the constraints before the target sentences. The constraints are separated by a special token <sep>. We use <pad> as the placeholder for sentence pairs with less than three constraint pairs. Here is an example:

# Source corpus
您 对 此 有 何 看法 ?
截至 发@@ 稿 时 , 经过 各族 群众 和 部队 官兵 连续 20 多 个 小时 的 紧急 抢修 , 麻@@ 塔 公路 已 基本 恢复 通行 。
# Target corpus
<pad> <sep> <pad> <sep> <pad> <sep> <pad> <sep> <pad> <sep> <pad> <sep> what is you view on this matter ?
部队 官兵 <sep> troops <sep> 群众 <sep> mobilized <sep> 紧急 <sep> rush <sep> the ho@@ tian military sub - command mobilized more than 500 troops and civilians to rush repair the highway .

We then binarize the text corpora using the following command. Please refer to here for more details.

python $CODE_DIR/fairseq_cli/preprocess.py -s zh -t en \
    --joined-dictionary \
    --trainpref $trainpref \
    --validpref $validpref \
    --testpref $testpref \
    --destdir $data_bin \
    --workers 32

Step 2: Train the Vanilla Transformer Model

We train the vanilla model using the following command.

CUDA_VISIBLE_DEVICES=0,1,2,3 python $CODE_DIR/fairseq_cli/train.py $data_bin \
    --target-key-sep $index_for_sep \
    --ls-segment-indices "0,1" --ls-segment-weights "1,1" \
    --fp16 --seed 32 --ddp-backend no_c10d \
    -s zh -t en \
    --lr-scheduler inverse_sqrt --lr 0.0007 \
    --warmup-init-lr 1e-07 --warmup-updates 4000 \
    --max-update 50000 \
    --weight-decay 0.0 --clip-norm 0.0 --dropout 0.3 \
    --max-tokens 8192 --update-freq 1 \
    --arch transformer --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9, 0.98)' \
    --save-dir $CKPTS \
    --tensorboard-logdir $LOGS \
    --criterion label_smoothed_cross_entropy \
    --label-smoothing 0.1 \
    --no-progress-bar --log-format simple --log-interval 10 \
    --no-epoch-checkpoints \
    --save-interval-updates 1000 --keep-interval-updates 5 \
    |& tee -a $LOGS/train.log

Step 3: Integrate Vectorized Constraints

We then train the constraint-aware model based on the checkpoint of the vanilla model.

CUDA_VISIBLE_DEVICES=0,1,2,3 python $CODE_DIR/fairseq_cli/train.py $data_bin \
    --finetune-from-model $path_to_vanilla_ckpt \
    --target-kv-table --target-key-sep $index_for_sep \
    --ls-segment-indices "0,1" --ls-segment-weights "$beta,$alpha" \
    --lambda-rank-reg 0 \
    --kv-attention-dropout 0.1 --kv-projection-dropout 0.1 \
    --plug-in-type type2 --plug-in-forward bottom --plug-in-component encdec \
    --plug-in-project none --aggregator-v-project --plug-in-v-project --plug-in-k-project \
    --plug-in-mid-dim 512 \
    --lr-scheduler cosine --lr 1e-7 --max-lr 1e-4 \
    --warmup-init-lr 1e-07 --warmup-updates 4000 \
    --lr-shrink 1 --lr-period-updates 6000 --max-update 10000 \
    --fp16 --seed 32 --ddp-backend no_c10d \
    -s zh -t en \
    --weight-decay 0.0 --clip-norm 0.0 --dropout 0.1 \
    --max-tokens 8192 --update-freq 1 \
    --arch transformer --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9, 0.98)' \
    --save-dir $CKPTS \
    --tensorboard-logdir $LOGS \
    --criterion label_smoothed_cross_entropy \
    --label-smoothing 0.1 \
    --no-progress-bar --log-format simple --log-interval 10 \
    --no-epoch-checkpoints \
    --save-interval-updates 1000 --keep-interval-updates 1 \
    |& tee -a $LOGS/train.log

Step 4: Generate with Constraints

We use the following command to translate with constraints.

CUDA_VISIBLE_DEVICES=0 python $CODE_DIR/generate.py $data_bin \
    --fp16 \
    -s zh -t en \
    --path $path_to_last_ckpt --gen-subset test \
    --beam 4 \
    --batch-size 128 \
    --target-key-sep $index_for_sep

Citation

Please cite as:

@inproceedings{Wang:2022:VecConstNMT,
  title = {Integrating Vectorized Lexical Constraints for Neural Machine Translation},
  author = {Shuo Wang, Zhixing Tan, Yang Liu},
  booktitle = {Proceedings of ACL 2022},
  year = {2022},

Contact

If you have questions, suggestions and bug reports, please email [email protected].