Fast Neural Machine Translation in C++
Works with commit: 3833669
This page provides data and model files for our shared task winning APE system described in Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing. If you use any of the data, systems or ideas, please cite:
@InProceedings{junczysdowmunt-grundkiewicz:2016:WMT,
author = {Junczys-Dowmunt, Marcin and Grundkiewicz, Roman},
title = {Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing},
booktitle = {Proceedings of the First Conference on Machine Translation},
month = {August},
year = {2016},
address = {Berlin, Germany},
publisher = {Association for Computational Linguistics},
pages = {751--758},
url = {http://www.aclweb.org/anthology/W16-2378}
}
Download the training data (514M)
This file contains the artificially generated post-editing triplets described in Table 1 of the paper. “4M” is the larger set denoted as “round-trip.n10” in that table, 500K is the smaller set denoted as “round-trip.n1”. The 20 times oversampled original training data for the shared task is not included, but can be obtained from the original shared task page.
data
├── 4M
│ ├── 4M.mt
│ ├── 4M.pe
│ └── 4M.src
└── 500K
├── 500K.mt
├── 500K.pe
└── 500K.src
Download the systems (2.7G)
We also provide the complete primary system and two contrastive variants. To create the submitted output, locate the Makefile and provide the path to the main directory of your working Marian tool (latest master, see Readme) in the following line:
AMUNMT=/home/marcinj/Badania/marian
Next type make. The included files should provide all input files, model files and scripts to produce our exact submission. You may need to change the number of GPU devices, as the original configs assume three GPUs. In the end you should see the three submission files:
AMU_ensemble8-mt+src_PRIMARY
AMU_ensemble4-mt_CONTRASTIVE
AMU_ensemble4-src_CONTRASTIVE
The configuration file for the best ensemble models/configs/mtsrc-pe.ensemble.ape.tuned.yml has been included below. It assumes the presence and availability of three GPUs in the line devices: [0, 1, 2], you want to change it to one device by devices: [0].
# amunn config file
relative-paths: yes
# Scorer configuration
scorers:
F0:
type: Nematus
path: ../mt-pe/model.iter260000.npz
F1:
type: Nematus
path: ../mt-pe/model.iter270000.npz
F2:
type: Nematus
path: ../mt-pe/model.iter280000.npz
F3:
type: Nematus
path: ../mt-pe/model.iter290000.npz
F4:
type: Nematus
path: ../src-pe/model.iter340000.npz
tab: 1
F5:
type: Nematus
path: ../src-pe/model.iter350000.npz
tab: 1
F6:
type: Nematus
path: ../src-pe/model.iter360000.npz
tab: 1
F7:
type: Nematus
path: ../src-pe/model.iter370000.npz
tab: 1
F8:
type: APE
source-vocab:
- ../mt-pe/vocab.mt.json
- ../src-pe/vocab.src.json
target-vocab: ../mt-pe/vocab.pe.json
weights:
F0: 0.0679875234050288
F1: 0.136272622440232
F2: 0.0447424881348462
F3: 0.0505810091549122
F4: 0.119029214497868
F5: -0.0291262004966649
F6: -0.0348248568202612
F7: 0.131424048800743
F8: 0.386012036249443
beam-size: 12
normalize: yes
n-best: no
devices: [0, 1, 2]
threads-per-device: 1
In the future we will provide more hints on how to train a similar system. Currently we supply the following files:
system
├── data
│ ├── de.bpe
│ ├── en.bpe
│ ├── true.de
│ └── true.en
├── Makefile
├── models
│ ├── configs
│ │ ├── mt-pe.ensemble4.tuned.yml
│ │ ├── mtsrc-pe.ensemble.ape.tuned.yml
│ │ └── src-pe.ensemble4.yml
│ ├── mt-pe
│ │ ├── model.iter260000.npz
│ │ ├── model.iter270000.npz
│ │ ├── model.iter280000.npz
│ │ ├── model.iter290000.npz
│ │ ├── vocab.mt.json
│ │ └── vocab.pe.json
│ └── src-pe
│ ├── model.iter340000.npz
│ ├── model.iter350000.npz
│ ├── model.iter360000.npz
│ ├── model.iter370000.npz
│ ├── vocab.pe.json
│ └── vocab.src.json
├── scripts
│ ├── apply_bpe.py
│ ├── deescape-special-chars.perl
│ ├── detruecase.perl
│ ├── escape-special-chars.perl
│ ├── prepare_submission.py
│ └── truecase.perl
└── test
├── test.mt
└── test.src
where data contains truecasing models and BPE codes. models/configs provides the configuration files for amun to load the model ensembles located in mt-pe (monolingual model, trained on MT-output and post-editing data) and src-pe (bilingual model, trained on source and post-editing data). test contains the blind test set, ground truth and evaluation scripts are again available from the shared task page.