July 9, 2019

493 words 3 mins read



Code for the Eager Translation Model from the paper You May Not Need Attention

repo name ofirpress/YouMayNotNeedAttention
repo link https://github.com/ofirpress/YouMayNotNeedAttention
language Python
size (curr.) 33 kB
stars (curr.) 302
created 2018-10-30

You May Not Need Attention

Code for the Eager Translation Model from the paper You May Not Need Attention by Ofir Press and Noah A. Smith.

Eager Translation Model

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+


Get the translation data

  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
  1. Enter the directory containing the bye-pair encoded (BPE) version of the data:
cd ./sockeye_autopilot/systems/wmt14_en_de/data/bpe
  1. Unzip everything
gunzip *
  1. 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

  1. Combine the source and target training data into one file
paste -d ' ||| ' train.shuf.src - - - - train.shuf.trg < /dev/null > combined_srctrg
  1. 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
  1. 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.


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


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.


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

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


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

comments powered by Disqus