Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)

This page includes instructions for training models described in Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019).

Training a joint alignment-translation model on WMT'18 En-De

1. Extract and preprocess the WMT'18 En-De data

./prepare-wmt18en2de_no_norm_no_escape_no_agressive.sh

2. Generate alignments from statistical alignment toolkits e.g. Giza++/FastAlign.

In this example, we use FastAlign.

git clone git@github.com:clab/fast_align.git
pushd fast_align
mkdir build
cd build
cmake ..
make
popd
ALIGN=fast_align/build/fast_align
paste bpe.32k/train.en bpe.32k/train.de | awk -F '\t' '{print $1 " ||| " $2}' > bpe.32k/train.en-de
$ALIGN -i bpe.32k/train.en-de -d -o -v > bpe.32k/train.align

3. Preprocess the dataset with the above generated alignments.

fairseq-preprocess \
    --source-lang en --target-lang de \
    --trainpref bpe.32k/train \
    --validpref bpe.32k/valid \
    --testpref bpe.32k/test \
    --align-suffix align \
    --destdir binarized/ \
    --joined-dictionary \
    --workers 32

4. Train a model

fairseq-train \
    binarized \
    --arch transformer_wmt_en_de_big_align --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 --activation-fn relu\
    --lr 0.0002 --lr-scheduler inverse_sqrt --warmup-updates 4000 --warmup-init-lr 1e-07 \
    --dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \
    --max-tokens 3500 --label-smoothing 0.1 \
    --save-dir ./checkpoints --log-interval 1000 --max-update 60000 \
    --keep-interval-updates -1 --save-interval-updates 0 \
    --load-alignments --criterion label_smoothed_cross_entropy_with_alignment \
    --fp16

Note that the --fp16 flag requires you have CUDA 9.1 or greater and a Volta GPU or newer.

If you want to train the above model with big batches (assuming your machine has 8 GPUs):

add --update-freq 8 to simulate training on 8x8=64 GPUs
increase the learning rate; 0.0007 works well for big batches

5. Evaluate and generate the alignments (BPE level)

fairseq-generate \
    binarized --gen-subset test --print-alignment \
    --source-lang en --target-lang de \
    --path checkpoints/checkpoint_best.pt --beam 5 --nbest 1

6. Other resources.

The code for:

preparing alignment test sets
converting BPE level alignments to token level alignments
symmetrizing bidirectional alignments
evaluating alignments using AER metric can be found here

Citation

@inproceedings{garg2019jointly,
  title = {Jointly Learning to Align and Translate with Transformer Models},
  author = {Garg, Sarthak and Peitz, Stephan and Nallasamy, Udhyakumar and Paulik, Matthias},
  booktitle = {Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  address = {Hong Kong},
  month = {November},
  url = {https://arxiv.org/abs/1909.02074},
  year = {2019},
}

1991wangliang / fairseq

Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)

Training a joint alignment-translation model on WMT'18 En-De

1. Extract and preprocess the WMT'18 En-De data

2. Generate alignments from statistical alignment toolkits e.g. Giza++/FastAlign.

3. Preprocess the dataset with the above generated alignments.

4. Train a model

5. Evaluate and generate the alignments (BPE level)

6. Other resources.

Citation

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Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)

Training a joint alignment-translation model on WMT'18 En-De

1. Extract and preprocess the WMT'18 En-De data

2. Generate alignments from statistical alignment toolkits e.g. Giza++/FastAlign.

3. Preprocess the dataset with the above generated alignments.

4. Train a model

5. Evaluate and generate the alignments (BPE level)

6. Other resources.

Citation

简介

发行版

贡献者

近期动态

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1991wangliang / fairseq