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An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing |
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This page provides data, model files and training instructions for our APE system described in An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing. If you use any of the data, systems or ideas, please cite:
@InProceedings{junczysdowmunt-grundkiewicz:2016:WMT,
author = {Marcin Junczys-Dowmunt and Roman Grundkiewicz},
title = {An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing},
year = {2017},
url = {https://arxiv.org/abs/1706.04138}
}
Create a new working folder and enter that folder:
mkdir ape-explore
cd ape-explore
Download and compile Marian. This is our training and translation toolkit.
git clone https://github.com/marian-nmt/marian
cd marian
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j16
cd ../..
Download and compile Moses. We use the TER scorer in Moses for reporting of validation results during training.
git clone https://github.com/moses-smt/mosesdecoder
cd mosesdecoder
./bjam -j16
cd ..
Obtain the already preprocessed training data as described below. The data has been prepared in such a way that training can be executed immediately. The included Makefile can be used to retrace the preprocessing steps. The folder train/data.all contains the raw unprocessed training and test data. The data has been created using the official training data for the WMT 2016 APE task and the artifical data made available here.
wget http://data.statmt.org/romang/ape-explore/train.tgz
tar -xzf train.tgz
cd train
Inside the train folder there is a set of YAML configuration files, one for each model type introduced in our paper. The configuration files can also be obtained separatly here.
Train a model by using its configuration file. Here we chose the dual-source model m-cgru from the paper.
Type the following command to train on four GPUs:
../marian/build/marian -c config/m-cgru.yml -d 0 1 2 3
which will produce model files in the models/m-cgru folder. Example output during training would look roughly like this:
[2017-07-10 23:28:47] Ep. 1 : Up. 1000 : Sen. 64000 : Cost 129.47 : Time 201.87s : 5814.94 words/s
[2017-07-10 23:31:33] Ep. 1 : Up. 2000 : Sen. 128000 : Cost 104.59 : Time 166.06s : 7060.59 words/s
[2017-07-10 23:34:16] Ep. 1 : Up. 3000 : Sen. 192000 : Cost 89.37 : Time 163.62s : 7185.44 words/s
[2017-07-10 23:37:02] Ep. 1 : Up. 4000 : Sen. 256000 : Cost 77.24 : Time 166.10s : 7062.50 words/s
[2017-07-10 23:39:47] Ep. 1 : Up. 5000 : Sen. 320000 : Cost 69.96 : Time 164.44s : 7174.16 words/s
[2017-07-10 23:42:33] Ep. 1 : Up. 6000 : Sen. 384000 : Cost 63.79 : Time 166.52s : 7034.91 words/s
[2017-07-10 23:45:22] Ep. 1 : Up. 7000 : Sen. 448000 : Cost 59.78 : Time 168.35s : 6984.08 words/s
[2017-07-10 23:48:06] Ep. 1 : Up. 8000 : Sen. 512000 : Cost 56.00 : Time 164.65s : 7117.06 words/s
[2017-07-10 23:50:50] Ep. 1 : Up. 9000 : Sen. 576000 : Cost 53.39 : Time 163.57s : 7176.63 words/s
[2017-07-10 23:53:34] Ep. 1 : Up. 10000 : Sen. 640000 : Cost 51.26 : Time 164.56s : 7145.62 words/s
[2017-07-10 23:53:34] Saving model to models/m-cgru/model.iter10000.npz
[2017-07-10 23:53:35] Saving model to models/m-cgru/model.npz
[2017-07-10 23:53:38] [valid] 10000 : cross-entropy : 38.332 : new best
[2017-07-10 23:53:38] Saving model to models/m-cgru/model.npz.dev.npz
[2017-07-10 23:54:22] [valid] 10000 : valid-script : 0.6483 : new best
[2017-07-10 23:57:06] Ep. 1 : Up. 11000 : Sen. 704000 : Cost 48.64 : Time 211.14s : 5554.63 words/s
[2017-07-10 23:59:53] Ep. 1 : Up. 12000 : Sen. 768000 : Cost 47.09 : Time 167.37s : 7012.21 words/s
The reported figures coming from the validation script are 1-TER as produced by the Moses scorer, thus higher is better.
Inspect models/m-cgru/validate.sh or the example below to see how translation and validation works. For ensembling back-up the recently trained model (or move the folder models/m-cgru) and restart training with the same config file. Weights will be initialized to different random weights.
In order to create and validate the averaged model (an element-wise average of model weights) run:
./models/m-cgru/validate_avg.sh
This will create the averaged model m-cgru/model.avg.npz and validate on the official WMT 2016 test set, reporting BLEU with ../mosesdecoder/scripts/generic/multi-bleu.pl and the official APE task scorer from eval. Results for averaged models from one training run are usually slightly better. Do not attempt to average models from different training runs, this will not work.
Coming soon.