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# Simple and Effective Noisy Channel Modeling for Neural Machine Translation (Yee et al., 2019) |
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This page contains pointers to pre-trained models as well as instructions on how to run the reranking scripts. |
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## Citation: |
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```bibtex |
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@inproceedings{yee2019simple, |
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title = {Simple and Effective Noisy Channel Modeling for Neural Machine Translation}, |
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author = {Kyra Yee and Yann Dauphin and Michael Auli}, |
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booktitle = {Conference on Empirical Methods in Natural Language Processing}, |
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year = {2019}, |
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} |
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``` |
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## Pre-trained Models: |
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Model | Description | Download |
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---|---|--- |
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`transformer.noisychannel.de-en` | De->En Forward Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/forward_de2en.tar.bz2) |
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`transformer.noisychannel.en-de` | En->De Channel Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/backward_en2de.tar.bz2) |
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`transformer_lm.noisychannel.en` | En Language model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/reranking_en_lm.tar.bz2) |
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Test Data: [newstest_wmt17](https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/wmt17test.tar.bz2) |
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## Example usage |
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``` |
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mkdir rerank_example |
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curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/forward_de2en.tar.bz2 | tar xvjf - -C rerank_example |
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curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/backward_en2de.tar.bz2 | tar xvjf - -C rerank_example |
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curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/reranking_en_lm.tar.bz2 | tar xvjf - -C rerank_example |
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curl https://dl.fbaipublicfiles.com/fairseq/models/noisychannel/wmt17test.tar.bz2 | tar xvjf - -C rerank_example |
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beam=50 |
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num_trials=1000 |
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fw_name=fw_model_ex |
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bw_name=bw_model_ex |
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lm_name=lm_ex |
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data_dir=rerank_example/hyphen-splitting-mixed-case-wmt17test-wmt14bpe |
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data_dir_name=wmt17 |
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lm=rerank_example/lm/checkpoint_best.pt |
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lm_bpe_code=rerank_example/lm/bpe32k.code |
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lm_dict=rerank_example/lm/dict.txt |
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batch_size=32 |
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bw=rerank_example/backward_en2de.pt |
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fw=rerank_example/forward_de2en.pt |
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# reranking with P(T|S) P(S|T) and P(T) |
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python examples/noisychannel/rerank_tune.py $data_dir --tune-param lenpen weight1 weight3 \ |
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--lower-bound 0 0 0 --upper-bound 3 3 3 --data-dir-name $data_dir_name \ |
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--num-trials $num_trials --source-lang de --target-lang en --gen-model $fw \ |
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-n $beam --batch-size $batch_size --score-model2 $fw --score-model1 $bw \ |
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--backwards1 --weight2 1 \ |
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-lm $lm --lm-dict $lm_dict --lm-name en_newscrawl --lm-bpe-code $lm_bpe_code \ |
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--model2-name $fw_name --model1-name $bw_name --gen-model-name $fw_name |
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# reranking with P(T|S) and P(T) |
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python examples/noisychannel/rerank_tune.py $data_dir --tune-param lenpen weight3 \ |
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--lower-bound 0 0 --upper-bound 3 3 --data-dir-name $data_dir_name \ |
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--num-trials $num_trials --source-lang de --target-lang en --gen-model $fw \ |
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-n $beam --batch-size $batch_size --score-model1 $fw \ |
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-lm $lm --lm-dict $lm_dict --lm-name en_newscrawl --lm-bpe-code $lm_bpe_code \ |
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--model1-name $fw_name --gen-model-name $fw_name |
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# to run with a preconfigured set of hyperparameters for the lenpen and model weights, using rerank.py instead. |
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python examples/noisychannel/rerank.py $data_dir \ |
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--lenpen 0.269 --weight1 1 --weight2 0.929 --weight3 0.831 \ |
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--data-dir-name $data_dir_name --source-lang de --target-lang en --gen-model $fw \ |
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-n $beam --batch-size $batch_size --score-model2 $fw --score-model1 $bw --backwards1 \ |
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-lm $lm --lm-dict $lm_dict --lm-name en_newscrawl --lm-bpe-code $lm_bpe_code \ |
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--model2-name $fw_name --model1-name $bw_name --gen-model-name $fw_name |
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``` |
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