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KTNMT

Introduction

Source code for the ACL 2023 main conference long paper "Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation" (Outstanding Paper Award)

In this work, we propose a knowledge transfer method that can efficiently adapt original MNMT models to diverse incremental language pairs.

Get Started

(Core) Data Preprocessing.

Standard BPE Procedure: following https://github.com/google/sentencepiece with 64k merged BPE tokens.

After obtaining the original vocabulary and the incremental vocabulary, you must get an incremental vocabulary of external models.

Model Training:

This system has been tested in the following environment.

Python version == 3.7

Pytorch version == 1.8.0

Fairseq version == 0.12.0 (pip install fairseq)

Note that it only influences the training procedure of the original and incremental model. You can choose your favorite deep learning library for model training.

Incremental Learning

We build the incremental learning procedure for Multilingual Neural Machine Translation as follows:

1. Get original multilingual translation models (or train a multilingual translation model by yourself).

2. Preprocessing incremental data

• Data Clean (optional, if needed)
• Get Vocabulary (optional, if needed, follow standard BPE procedure)

3. Incremental Training. We provide all runing scripts in the folder ''src''. Here is an example:

   echo "Task format: "
   export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
   export SEED=222
   export CHECKPOINT_PATH='' # save new model
   export CL_CHECKPOINT_PATH='' # old model
   export DATA_PATH='' # incremental data bin
   export USER_PATH='mkt/' # user dir
   export ARCH=transformer_adapter
   export TASK=translation_multi_adapter
   export ADAPTER_MODULE=serial # serial / parallel / parallel_gate / parallel_norm / parallel_dp
   export KRAIL_MODULE='' # krail / krail_ffn / krail_map / krail_map_last / krail_map_avg / krail_adaptive / krail_adaptive_final
   export LANG_DICT='misc/cl_adapter/lang_dicts_uk.txt' # new lang dict
   export KRAIL_MODEL='mPLM/418M_last_checkpoint.pt' # external model path
   export NEW_DICT_PATH='misc/cl_adapter/model_dict.m2m100.vex.txt' # combination model dict, we can get this vocab by scripts
   export KRAIL_DICT_PATH='misc/m2m100/model_dict.128k.txt' # external model dict
   export lang_pairs=en-uk
   export CLIP_NORM=0.0
   export OPTIMIZER=adam
   export ADAM_EPS=1e-9
   export LR=5e-4
   export LR_SCHEDULER=inverse_sqrt
   export WARMUP=4000
   export DROPOUT=0.2
   export ATT_DROPOUT=0.2
   export WEIGHT_DECAY=0.0001
   export CRITERION=label_smoothed_cross_entropy
   export LABEL_SMOOTHING=0.1
   export MAX_TOKENS=1024
   export UPDATE_FREQ=8
   export SAVE_INTERVAL_UPDATES=2500
   export KEEP_INTERVAL_UPDATES=1
   export MAX_UPDATE=500000
   
   fairseq-train $DATA_PATH \
   --user-dir $USER_PATH \
   --share-all-embeddings \
   --encoder-normalize-before --decoder-normalize-before \
   --encoder-embed-dim 1024 --encoder-ffn-embed-dim 8192 --encoder-attention-heads 16 \
   --decoder-embed-dim 1024 --decoder-ffn-embed-dim 8192 --decoder-attention-heads 16 \
   --encoder-layers 24 --decoder-layers 24 \
   --left-pad-source False --left-pad-target False \
   --arch $ARCH \
   --task $TASK \
   --sampling-method temperature \
   --sampling-temperature 5 \
   --lang-tok-style multilingual \
   --lang-dict $LANG_DICT \
   --lang-pairs $lang_pairs \
   --encoder-langtok src \
   --decoder-langtok \
   --clip-norm $CLIP_NORM \
   --optimizer $OPTIMIZER \
   --adam-betas '(0.9, 0.98)' \
   --adam-eps $ADAM_EPS \
   --lr $LR \
   --lr-scheduler $LR_SCHEDULER \
   --warmup-updates $WARMUP \
   --dropout $DROPOUT \
   --attention-dropout $ATT_DROPOUT \
   --weight-decay $WEIGHT_DECAY \
   --criterion $CRITERION \
   --label-smoothing $LABEL_SMOOTHING \
   --max-tokens $MAX_TOKENS \
   --save-dir $CHECKPOINT_PATH/checkpoints/ \
   --update-freq $UPDATE_FREQ \
   --save-interval-updates $SAVE_INTERVAL_UPDATES \
   --keep-interval-updates $KEEP_INTERVAL_UPDATES \
   --max-update $MAX_UPDATE \
   --no-epoch-checkpoints \
   --seed $SEED --log-format simple --log-interval 300 \
   --patience 10 \
   --tensorboard-logdir $CHECKPOINT_PATH/logs/ \
   --no-progress-bar \
   --ddp-backend no_c10d \
   --finetune-from-model $CL_CHECKPOINT_PATH \
   --freeze-all \
   --adapter-inner-dims 4096 \
   --adapter-module $ADAPTER_MODULE \
   --krail-module $KRAIL_MODULE \
   --vocab-adapter \
   --base-embed-dim 32211 \
   --krail-model $KRAIL_MODEL \
   --new-dict-path $NEW_DICT_PATH \
   --krail-dict-path $KRAIL_DICT_PATH
   

Inference & Evaluation

Please refer to src/inference.sh & run_sh/evaluate.sh

Citation

@inproceedings{huang-etal-2023-knowledge,
    title = "Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation",
    author = "Huang, Kaiyu  and
      Li, Peng  and
      Ma, Jin  and
      Yao, Ting  and
      Liu, Yang",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.852",
    doi = "10.18653/v1/2023.acl-long.852",
    pages = "15286--15304",
}