You May Not Need Attention

git clone ...
bash scripts/make_virtualenv.sh
source scripts/../venvs//YouMayNotNeedAttention/bin/activate
bash scripts/download_install_packages.sh

cp combined_srctrg combined_srctrg_clean
sed -i '/^ |||/d' combined_srctrg_clean
sed -i '/ ||| $/d' combined_srctrg_clean

cat combined_srctrg | grep -nr '^ ||| ' | wc -l
cat combined_srctrg | grep -nr ' ||| $' | wc -l

Code for the Eager Translation Model from the paper You May Not Need Attention by Ofir Press and Noah A. Smith with some adjustments by Markus Göckeritz. Adjustments do not include any changes to the functionality or behavior of the code by the original authors.

The following python packages are required:

• pytorch 0.4
• sacreBLEU
• mosestokenizer

In addition, fast_align is needed to compute alignments.

This code requires Python 3.6+

Preprocessing

Get the translation data

0. Setup & activate a virtual environment

bash scripts/make_virtualenv.sh
source venvs/YouMayNotNeedAttention/bin/activate

1. Install all dependencies

bash scripts/download_install_packages.sh

1. Download the dataset you'd like to use. For this example we'll use Sockeye Autopilot to download the WMT 2014 EN->DE dataset.

sockeye-autopilot --task wmt14_en_de --model none

2. Enter the directory containing the bye-pair encoded (BPE) version of the data:

cd ./sockeye_autopilot/systems/wmt14_en_de/data/bpe

3. Unzip everything

gunzip *

4. Shuffle the tranining data

paste -d '|'  train.src train.trg | shuf | awk -v FS='|' '{ print $1 > "train.shuf.src" ; print $2 > "train.shuf.trg" }'

Run the Eager Feasibility preprocessing

5. Combine the source and target training data into one file

paste -d ' ||| ' train.shuf.src - - - - train.shuf.trg < /dev/null > combined_srctrg

6. Use fast_align to find the aligments of the training sentence pairs

./fast_align -i ~/sockeye_autopilot/systems/wmt14_en_de/data/bpe/combined_srctrg -d -o -v > forward.align_ende

7. Run our script for making the training data Eager Feasible:

python  add_epsilons.py --align forward.align_ende --trg ~/sockeye_autopilot/systems/wmt14_en_de/data/bpe/to_train/train.shuf.trg --src ~/sockeye_autopilot/systems/wmt14_en_de/data/bpe/to_train/train.shuf.src --left_pad 4 --directory ~/corpus/WMTENDE/4pad/ 

Make sure the directory given for the --directory argument actually exists! The --left_pad argument specifies how many initial padding tokens should be inserted into the training dataset.

Note: The add_epsilons script may encounter sentence pairs which fast_align could not find an alignment for. If so, it will delete those lines from the training set. It will then ask you to re-run the script (with the same arguments) in order to finish the process.

Training

Use the following command to train:

python main.py --data ~/corpus/WMTENDE/4pad/  --save ~/exps/ --wdrop 0 --dropout 0.1 --dropouti 0.15 --dropouth 0.1 --dropoutcomb 0.1 --nlayer 4 --epochs 25 --bptt 60 --nhid 1000 --emsize 1000 --batch_size 200 --start_decaying_lr_step 200000 --update_interval 6500

These were the hyperparams used to train the models presented in the paper.

--save specifies where to store the model checkpoints

--nhid is the size of the LSTM

--emsize is double the word embedding size, and must be equivalent to --nhid

Translate

Once you have a trained model, you can use it to translate a document containing sentences in the source language.

python generate.py --checkpoint ~/exps/20181012-022002/model351000.pt  --data  ~/corpus/WMTENDE/4pad/    --src_path  ~/sockeye_autopilot/systems/wmt14_en_de/data/bpe/dev.src  --beam_size 5 --eval  --target_translation ~/sockeye_autopilot/systems/wmt14_en_de/data/raw/dev.trg  --epsilon_limit 3 --src_epsilon_injection 4  --start_pads 5 --language de --save_dir ./output/

This will translate the file found in --src_path , and will save the output in the --save_dir directory. In addition, it will compute the BLEU score.

Reference

If you found this code useful, please cite the following paper:

@article{press2018you,
  title={You May Not Need Attention},
  author={Press, Ofir and Smith, Noah A},
  journal={arXiv preprint arXiv:1810.13409},
  year={2018}
}

Acknowledgments

This repository is based on the code from the AWD-LSTM language model.