fairseq/examples/byte_level_bpe/README.md

Neural Machine Translation with Byte-Level Subwords

https://arxiv.org/abs/1909.03341

We provide an implementation of byte-level byte-pair encoding (BBPE), taking IWSLT 2017 Fr-En translation as example.

Data

Get data and generate fairseq binary dataset:

bash ./get_data.sh

Model Training

Train Transformer model with Bi-GRU embedding contextualization (implemented in gru_transformer.py):

# VOCAB=bytes
# VOCAB=chars
VOCAB=bbpe2048
# VOCAB=bpe2048
# VOCAB=bbpe4096
# VOCAB=bpe4096
# VOCAB=bpe16384

fairseq-train "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \
    --arch gru_transformer --encoder-layers 2 --decoder-layers 2 --dropout 0.3 --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9, 0.98)' \
    --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
    --log-format 'simple' --log-interval 100 --save-dir "checkpoints/${VOCAB}" \
    --batch-size 100 --max-update 100000 --update-freq 2

Generation

fairseq-generate requires bytes (BBPE) decoder to convert byte-level representation back to characters:

# BPE=--bpe bytes
# BPE=--bpe characters
BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe2048.model
# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe2048.model
# BPE=--bpe byte_bpe --sentencepiece-model-path data/spm_bbpe4096.model
# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe4096.model
# BPE=--bpe sentencepiece --sentencepiece-model data/spm_bpe16384.model

fairseq-generate "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \
    --source-lang fr --gen-subset test --sacrebleu --path "checkpoints/${VOCAB}/checkpoint_last.pt" \
    --tokenizer moses --moses-target-lang en ${BPE}

When using fairseq-interactive, bytes (BBPE) encoder/decoder is required to tokenize input data and detokenize model predictions:

fairseq-interactive "data/bin_${VOCAB}" --task translation --user-dir examples/byte_level_bpe/gru_transformer \
    --path "checkpoints/${VOCAB}/checkpoint_last.pt" --input data/test.fr --tokenizer moses --moses-source-lang fr \
    --moses-target-lang en ${BPE} --buffer-size 1000 --max-tokens 10000

Results

Vocabulary	Model	BLEU
Joint BPE 16k (Kudo, 2018)	512d LSTM 2+2	33.81
Joint BPE 16k	Transformer base 2+2 (w/ GRU)	36.64 (36.72)
Joint BPE 4k	Transformer base 2+2 (w/ GRU)	35.49 (36.10)
Joint BBPE 4k	Transformer base 2+2 (w/ GRU)	35.61 (35.82)
Joint BPE 2k	Transformer base 2+2 (w/ GRU)	34.87 (36.13)
Joint BBPE 2k	Transformer base 2+2 (w/ GRU)	34.98 (35.43)
Characters	Transformer base 2+2 (w/ GRU)	31.78 (33.30)
Bytes	Transformer base 2+2 (w/ GRU)	31.57 (33.62)

Citation

@misc{wang2019neural,
    title={Neural Machine Translation with Byte-Level Subwords},
    author={Changhan Wang and Kyunghyun Cho and Jiatao Gu},
    year={2019},
    eprint={1909.03341},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Contact

Changhan Wang (changhan@fb.com), Kyunghyun Cho (kyunghyuncho@fb.com), Jiatao Gu (jgu@fb.com)