wehub-resource-sync
127 lines
5.1 KiB
Markdown
127 lines
5.1 KiB
Markdown
# Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)
|
|
|
|
This page includes instructions for reproducing results from the paper [Unsupervised Quality Estimation for Neural
|
|
Machine Translation (Fomicheva et al., 2020)](https://arxiv.org/abs/2005.10608)
|
|
|
|
## Requirements:
|
|
|
|
* mosesdecoder: https://github.com/moses-smt/mosesdecoder
|
|
* subword-nmt: https://github.com/rsennrich/subword-nmt
|
|
* flores: https://github.com/facebookresearch/flores
|
|
|
|
## Download Models and Test Data
|
|
|
|
Download translation models and test data from [MLQE dataset repository](https://github.com/facebookresearch/mlqe).
|
|
|
|
## Set up:
|
|
|
|
Given a testset consisting of source sentences and reference translations:
|
|
|
|
* `SRC_LANG`: source language
|
|
* `TGT_LANG`: target language
|
|
* `INPUT`: input prefix, such that the file `$INPUT.$SRC_LANG` contains source sentences and `$INPUT.$TGT_LANG`
|
|
contains the reference sentences
|
|
* `OUTPUT_DIR`: output path to store results
|
|
* `MOSES_DECODER`: path to mosesdecoder installation
|
|
* `BPE_ROOT`: path to subword-nmt installation
|
|
* `BPE`: path to BPE model
|
|
* `MODEL_DIR`: directory containing the NMT model `.pt` file as well as the source and target vocabularies.
|
|
* `TMP`: directory for intermediate temporary files
|
|
* `GPU`: if translating with GPU, id of the GPU to use for inference
|
|
* `DROPOUT_N`: number of stochastic forward passes
|
|
|
|
`$DROPOUT_N` is set to 30 in the experiments reported in the paper. However, we observed that increasing it beyond 10
|
|
does not bring substantial improvements.
|
|
|
|
## Translate the data using standard decoding
|
|
|
|
Preprocess the input data:
|
|
```
|
|
for LANG in $SRC_LANG $TGT_LANG; do
|
|
perl $MOSES_DECODER/scripts/tokenizer/tokenizer.perl -threads 80 -a -l $LANG < $INPUT.$LANG > $TMP/preprocessed.tok.$LANG
|
|
python $BPE_ROOT/apply_bpe.py -c ${BPE} < $TMP/preprocessed.tok.$LANG > $TMP/preprocessed.tok.bpe.$LANG
|
|
done
|
|
```
|
|
|
|
Binarize the data for faster translation:
|
|
|
|
```
|
|
fairseq-preprocess --srcdict $MODEL_DIR/dict.$SRC_LANG.txt --tgtdict $MODEL_DIR/dict.$TGT_LANG.txt
|
|
--source-lang ${SRC_LANG} --target-lang ${TGT_LANG} --testpref $TMP/preprocessed.tok.bpe --destdir $TMP/bin --workers 4
|
|
```
|
|
|
|
Translate
|
|
|
|
```
|
|
CUDA_VISIBLE_DEVICES=$GPU fairseq-generate $TMP/bin --path ${MODEL_DIR}/${SRC_LANG}-${TGT_LANG}.pt --beam 5
|
|
--source-lang $SRC_LANG --target-lang $TGT_LANG --no-progress-bar --unkpen 5 > $TMP/fairseq.out
|
|
grep ^H $TMP/fairseq.out | cut -d- -f2- | sort -n | cut -f3- > $TMP/mt.out
|
|
```
|
|
|
|
Post-process
|
|
|
|
```
|
|
sed -r 's/(@@ )| (@@ ?$)//g' < $TMP/mt.out | perl $MOSES_DECODER/scripts/tokenizer/detokenizer.perl
|
|
-l $TGT_LANG > $OUTPUT_DIR/mt.out
|
|
```
|
|
|
|
## Produce uncertainty estimates
|
|
|
|
### Scoring
|
|
|
|
Make temporary files to store the translations repeated N times.
|
|
|
|
```
|
|
python ${SCRIPTS}/scripts/uncertainty/repeat_lines.py -i $TMP/preprocessed.tok.bpe.$SRC_LANG -n $DROPOUT_N
|
|
-o $TMP/repeated.$SRC_LANG
|
|
python ${SCRIPTS}/scripts/uncertainty/repeat_lines.py -i $TMP/mt.out -n $DROPOUT_N -o $TMP/repeated.$TGT_LANG
|
|
|
|
fairseq-preprocess --srcdict ${MODEL_DIR}/dict.${SRC_LANG}.txt $TGT_DIC --source-lang ${SRC_LANG}
|
|
--target-lang ${TGT_LANG} --testpref ${TMP}/repeated --destdir ${TMP}/bin-repeated
|
|
```
|
|
|
|
Produce model scores for the generated translations using `--retain-dropout` option to apply dropout at inference time:
|
|
|
|
```
|
|
CUDA_VISIBLE_DEVICES=${GPU} fairseq-generate ${TMP}/bin-repeated --path ${MODEL_DIR}/${LP}.pt --beam 5
|
|
--source-lang $SRC_LANG --target-lang $TGT_LANG --no-progress-bar --unkpen 5 --score-reference --retain-dropout
|
|
--retain-dropout-modules '["TransformerModel","TransformerEncoder","TransformerDecoder","TransformerEncoderLayer"]'
|
|
TransformerDecoderLayer --seed 46 > $TMP/dropout.scoring.out
|
|
|
|
grep ^H $TMP/dropout.scoring.out | cut -d- -f2- | sort -n | cut -f2 > $TMP/dropout.scores
|
|
|
|
```
|
|
|
|
Use `--retain-dropout-modules` to specify the modules. By default, dropout is applied in the same places
|
|
as for training.
|
|
|
|
Compute the mean of the resulting output distribution:
|
|
|
|
```
|
|
python $SCRIPTS/scripts/uncertainty/aggregate_scores.py -i $TMP/dropout.scores -o $OUTPUT_DIR/dropout.scores.mean
|
|
-n $DROPOUT_N
|
|
```
|
|
|
|
### Generation
|
|
|
|
Produce multiple translation hypotheses for the same source using `--retain-dropout` option:
|
|
|
|
```
|
|
CUDA_VISIBLE_DEVICES=${GPU} fairseq-generate ${TMP}/bin-repeated --path ${MODEL_DIR}/${LP}.pt
|
|
--beam 5 --source-lang $SRC_LANG --target-lang $TGT_LANG --no-progress-bar --retain-dropout
|
|
--unkpen 5 --retain-dropout-modules TransformerModel TransformerEncoder TransformerDecoder
|
|
TransformerEncoderLayer TransformerDecoderLayer --seed 46 > $TMP/dropout.generation.out
|
|
|
|
grep ^H $TMP/dropout.generation.out | cut -d- -f2- | sort -n | cut -f3- > $TMP/dropout.hypotheses_
|
|
|
|
sed -r 's/(@@ )| (@@ ?$)//g' < $TMP/dropout.hypotheses_ | perl $MOSES_DECODER/scripts/tokenizer/detokenizer.perl
|
|
-l $TGT_LANG > $TMP/dropout.hypotheses
|
|
```
|
|
|
|
Compute similarity between multiple hypotheses corresponding to the same source sentence using Meteor
|
|
evaluation metric:
|
|
```
|
|
python meteor.py -i $TMP/dropout.hypotheses -m <path_to_meteor_installation> -n $DROPOUT_N -o
|
|
$OUTPUT_DIR/dropout.gen.sim.meteor
|
|
```
|