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Non-autoregressive Neural Machine Translation (NAT)

This page mainly includes instructions for reproducing results from the following papers

We also provided our own implementations for several popular non-autoregressive-based models as reference:

Dataset

First, follow the instructions to download and preprocess the WMT'14 En-De dataset. Make sure to learn a joint vocabulary by passing the --joined-dictionary option to fairseq-preprocess.

Knowledge Distillation

Following Gu et al. 2019, knowledge distillation from an autoregressive model can effectively simplify the training data distribution, which is sometimes essential for NAT-based models to learn good translations. The easiest way of performing distillation is to follow the instructions of training a standard transformer model on the same data, and then decode the training set to produce a distillation dataset for NAT.

Download

We also provided the preprocessed original and distillation datasets. Please build the binarized dataset on your own.

Train a model

Then we can train a nonautoregressive model using the translation_lev task and a new criterion nat_loss. Use the --noise flag to specify the input noise used on the target sentences. In default, we run the task for Levenshtein Transformer, with --noise='random_delete'. Full scripts to run other models can also be found here.

The following command will train a Levenshtein Transformer on the binarized dataset.

fairseq-train \
    data-bin/wmt14_en_de_distill \
    --save-dir checkpoints \
    --ddp-backend=legacy_ddp \
    --task translation_lev \
    --criterion nat_loss \
    --arch levenshtein_transformer \
    --noise random_delete \
    --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9,0.98)' \
    --lr 0.0005 --lr-scheduler inverse_sqrt \
    --stop-min-lr '1e-09' --warmup-updates 10000 \
    --warmup-init-lr '1e-07' --label-smoothing 0.1 \
    --dropout 0.3 --weight-decay 0.01 \
    --decoder-learned-pos \
    --encoder-learned-pos \
    --apply-bert-init \
    --log-format 'simple' --log-interval 100 \
    --fixed-validation-seed 7 \
    --max-tokens 8000 \
    --save-interval-updates 10000 \
    --max-update 300000

Translate

Once a model is trained, we can generate translations using an iterative_refinement_generator which will based on the model's initial output and iteratively read and greedily refine the translation until (1) the model predicts the same translations for two consecutive iterations; or (2) the generator reaches the maximum iterations (--iter-decode-max-iter). Use --print-step to check the actual # of iteration for each sentence.

For Levenshtein Transformer, it sometimes helps to apply a --iter-decode-eos-penalty (typically, 0~3) to penalize the model finishing generation too early and generating too short translations.

For example, to generate with --iter-decode-max-iter=9:

fairseq-generate \
    data-bin/wmt14_en_de_distill \
    --gen-subset test \
    --task translation_lev \
    --path checkpoints/checkpoint_best.pt \
    --iter-decode-max-iter 9 \
    --iter-decode-eos-penalty 0 \
    --beam 1 --remove-bpe \
    --print-step \
    --batch-size 400

In the end of the generation, we can see the tokenized BLEU score for the translation.

Advanced Decoding Methods

Ensemble

The NAT models use special implementations of ensembling to support iterative refinement and a variety of parallel operations in different models, while it shares the same API as standard autoregressive models as follows:

fairseq-generate \
    data-bin/wmt14_en_de_distill \
    --gen-subset test \
    --task translation_lev \
    --path checkpoint_1.pt:checkpoint_2.pt:checkpoint_3.pt \
    --iter-decode-max-iter 9 \
    --iter-decode-eos-penalty 0 \
    --beam 1 --remove-bpe \
    --print-step \
    --batch-size 400

We use : to split multiple models. Note that, not all NAT models support ensembling for now.

Length-beam

For models that predict lengths before decoding (e.g. the vanilla NAT, Mask-Predict, etc), it is possible to improve the translation quality by varying the target lengths around the predicted value, and translating the same example multiple times in parallel. We can select the best translation with the highest scores defined by your model's output.

Note that, not all models support length beams. For models which dynamically change the lengths (e.g. Insertion Transformer, Levenshtein Transformer), the same trick does not apply.

Re-ranking

If the model generates multiple translations with length beam, we can also introduce an autoregressive model to rerank the translations considering scoring from an autoregressive model is much faster than decoding from that.

For example, to generate translations with length beam and reranking,

fairseq-generate \
    data-bin/wmt14_en_de_distill \
    --gen-subset test \
    --task translation_lev \
    --path checkpoints/checkpoint_best.pt:at_checkpoints/checkpoint_best.pt \
    --iter-decode-max-iter 9 \
    --iter-decode-eos-penalty 0 \
    --iter-decode-with-beam 9 \
    --iter-decode-with-external-reranker \
    --beam 1 --remove-bpe \
    --print-step \
    --batch-size 100

Note that we need to make sure the autoregressive model shares the same vocabulary as our target non-autoregressive model.

Citation

@incollection{NIPS2019_9297,
    title = {Levenshtein Transformer},
    author = {Gu, Jiatao and Wang, Changhan and Zhao, Junbo},
    booktitle = {Advances in Neural Information Processing Systems 32},
    editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
    pages = {11179--11189},
    year = {2019},
    publisher = {Curran Associates, Inc.},
    url = {http://papers.nips.cc/paper/9297-levenshtein-transformer.pdf}
}
@article{zhou2019understanding,
  title={Understanding Knowledge Distillation in Non-autoregressive Machine Translation},
  author={Zhou, Chunting and Neubig, Graham and Gu, Jiatao},
  journal={arXiv preprint arXiv:1911.02727},
  year={2019}
}