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