Skip to content

Latest commit

 

History

History
88 lines (77 loc) · 3.34 KB

README.md

File metadata and controls

88 lines (77 loc) · 3.34 KB
Raw

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}" \
    --max-sentences 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 ([email protected]), Kyunghyun Cho ([email protected]), Jiatao Gu ([email protected])