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Zeķacs (A fork of awslabs/sockeye)

Only differences between Sockeye and this fork are:

  • We added alignment matrix learning. (This works quite decently)
  • We added a janky way to read training data from stdin. (This scales baddly beyond a few GPUs)

Instructions on training models by using standard input/data streaming, for Tilde people.

This section refers to proprietary scripts used by Tilde, so if you stumbled upon this repo by accident, just ignore this.

Description of how one goes about training sockeye with data streaming.

  1. In order to run sockeye with data streaming you first have to get LetsMT mt-stream.
  • To install mt-stream see the mt-stream repo.
  • From this point onwards I'll just assume you have the mt-stream environment in a conda environment called mtstream_env.
  1. Generate vocabularies for the source, target and source or target factors.
conda run -n mtstream_env --no-capute-output python path/to/mtstream/main.py\
  --config ./example_config.yaml\
  --database-name ./example_database_name\
  --worker-count 8\
  --output-format json |
conda run -n mtstream_env --no-capture-output python path/to/mtstream/misc/MTStreamSockeyeVocabGen.py\
  --output ./vocabs\
  --share-vocab\
  --sample-count 5000000\

A bit of explanation for the command above:

  • The command will generate a vocabulary for the source, target, and source and target factors, and put them in the ./vocabs folder.
    • The name of the source vocabulary file will be vocab.src.0.json.
    • The names of source factor vocabulary files will be vocab.src.1.json, vocab.src.2.json,... for how-ever-many source factors you have.
    • The naming convention for target and target factor vocabularies is the same except src is replaced with trg, e.g. vocab.trg.0.json
  • conda run -n mtstream_env python path/to/mtstream/main.py runs mtstream.
    • The --no-capute-output flags just make it so that conda doesn't capture standard output.
    • --config ./example_config.yaml Ask Toms Bergmanis about how example_config.yaml has to be structured.
    • --database-name ./example_database_name - The first time MTStream is run it will generate a database file at this location.
    • --worker-count 8 - Amount of worker processes that will be launched by MTStream for generating data. (Feel free to change this)
    • --output-format json - Makes MTStream output loose json. This is necessary for using it with Zeķacs.
  • conda run -n mtstream_env python path/to/mtstream/misc/MTStreamSockeyeVocabGen.py runs a script that generates vocabulary files that can be used by Sockeye.
    • --output ./vocabs - Path to folder where MTStreamVocabGen.py will output the source, target and factor vocabulary files.
    • For more details on other parameters run misc/MTStreamSockeyeVocabGen.py --help
  • Warning: I don't remember if the command actually halts when it's done, maybe it required a manual killing.
  1. Next you need to install sockeye:
git clone https://github.com/tilde-nlp/sockeye.git
conda create -n sockeye_env python=3.8
conda activate sockeye_env
cd sockeye
pip install -r requirements/requirements.txt
pip install .
  1. Now we can actually run the training. To train on a single GPU you can run:
conda run -n mtstream_env --no-capute-output python /path/to/mtstream/main.py\
  --config ./example_config.yaml\
  --database-name ./example_database_name\
  --worker-count 8\
  --output-format json |
conda run -n mtstream_env --no-capture-output python /path/to/mtstream/misc/MTStreamSockeyeAdapter.py\
  --nproc 1\
  --max-seq-len 191\
  --batch-size 9000 |
conda run -n sockeye_env --no-capture-output python /path/to/sockeye/sockeye/train.py\
  --stdin-input\
  --batch-size 9000\
  --max-seq-len 191:191\
  --source-vocab ./vocabs/vocab.src.0.json\
  --target-vocab ./vocabs/vocab.trg.0.json\
  --source-factor-vocabs ./vocabs/vocabs.src.1.json ./vocabs/vocab.src.2.json ...<Add however many source factors you have>\
  --target-factor-vocabs ./vocabs/vocabs.trg.1.json ./vocabs/vocab.trg.2.json ...<Add however many target factors you have>\
  <Other sockeye args>

A bit of explanation about the commands above:

  • The command above:
    • Runs MTStream.
    • Pipes the output to MTStreamSockeyeAdapter.py.
    • MTStreamSockeyeAdapter.py buckets and batches data for sockeye.
    • Pipes the output to sockeye.
    • Sockeye performs training.
  • The --config ./example_config.yaml passed to mtstream/main.py must be the same config as config used when creating vocabularies in step 2.
  • --nproc 1 - Tells MTStreamSockeyeAdapter.py that sockeye is using only 1 GPU.
  • --max-seq-len has to have the same value for both sockeye/sockeye/train and mtstream/misc/MTStreamSockeyeAdapter.py.
  • --batch-size maximum size of padded batch in tokens.
  • Run MTStreamSockeyeAdapter.py --help for more details.
  • Sidenote, when using --stdin-input Sockeye currently supports only square buckets.
  1. To run sockeye with data streaming on multiple GPUs run:
conda run -n mtstream_env --no-capute-output python /path/to/mtstream/main.py\
  --config ./example_config.yaml\
  --database-name ./example_database_name\
  --worker-count 8\
  --output-format json |
conda run -n mtstream_env --no-capture-output python /path/to/mtstream/misc/MTStreamSockeyeAdapter.py\
  --nproc 4\
  --max-seq-len 191\
  --batch-size 9000 |
conda run -n sockeye_env --no-capture-output python -m torch.distributed.run --no_python --nproc_per_node 4 python -m /path/to/sockeye/sockeye/train.py\
  --dist\
  --stdin-input\
  --batch-size 9000\
  --max-seq-len 191:191\
  --source-vocab ./vocabs/vocab.src.0.json\
  --target-vocab ./vocabs/vocab.trg.0.json\
  --source-factor-vocabs ./vocabs/vocabs.src.1.json ./vocabs/vocab.src.2.json ...<Add however many source factors you have>\
  --target-factor-vocabs ./vocabs/vocabs.trg.1.json ./vocabs/vocab.trg.2.json ...<Add however many target factors you have>\
  <Other sockeye args>

A bit of explanation:

  • nproc_per_node 4 - Number of GPUs used by Sockeye. Has to be the same value as --nproc passed to MTStreamSockeyeAdapter.py.

Sockeye [From here downwards it's just the original readme from awslabs]

PyPI version GitHub license GitHub issues Documentation Status Torch Nightly

Sockeye is an open-source sequence-to-sequence framework for Neural Machine Translation built on PyTorch. It implements distributed training and optimized inference for state-of-the-art models, powering Amazon Translate and other MT applications. Recent developments and changes are tracked in our CHANGELOG.

For a quickstart guide to training a standard NMT model on any size of data, see the WMT 2014 English-German tutorial.

For questions and issue reports, please file an issue on GitHub.

Version 3.1.x: PyTorch only

With version 3.1.x, we remove support for MXNet 2.x. Models trained with PyTorch and Sockeye 3.0.x remain compatible with Sockeye 3.1.x. Models trained with 2.3.x (using MXNet) and converted to PyTorch with Sockeye 3.0.x's conversion tool can NOT be used with Sockeye 3.1.x.

Version 3.0.0: Concurrent PyTorch and MXNet support

Starting with version 3.0.0, Sockeye is also based on PyTorch. We maintain backwards compatibility with MXNet models of version 2.3.x with 3.0.x. If MXNet 2.x is installed, Sockeye can run both with PyTorch or MXNet.

All models trained with 2.3.x (using MXNet) can be converted to models running with PyTorch using the converter CLI (sockeye.mx_to_pt). This will create a PyTorch parameter file (<model>/params.best) and backup the existing MXNet parameter file to <model>/params.best.mx. Note that this only applies to fully-trained models that are to be used for inference. Continued training of an MXNet model with PyTorch is not supported (because we do not convert training and optimizer states). sockeye.mx_to_pt requires MXNet to be installed into the environment.

All CLIs of Version 3.0.0 now use PyTorch by default, e.g. sockeye-{train,translate,score}. MXNet-based CLIs/modules are still operational and accessible via sockeye-{train,translate,score}-mx.

Sockeye 3 can be installed and run without MXNet, but if installed, an extended test suite is executed to ensure equivalence between PyTorch and MXNet models. Note that running Sockeye 3.0.0 with MXNet requires MXNet 2.x to be installed (pip install --pre -f https://dist.mxnet.io/python 'mxnet>=2.0.0b2021')

Installation

Download the current version of Sockeye:

git clone https://github.com/awslabs/sockeye.git

Install the sockeye module and its dependencies:

cd sockeye && pip3 install --editable .

For faster GPU training, install NVIDIA Apex. NVIDIA also provides PyTorch Docker containers that include Apex.

Documentation

Older versions

  • Sockeye 3.0, based on PyTorch & MXNet 2.x is available in the sockeye_30 branch.
  • Sockeye 2.x, based on the MXNet Gluon API, is available in the sockeye_2 branch.
  • Sockeye 1.x, based on the MXNet Module API, is available in the sockeye_1 branch.

Citation

For more information about Sockeye, see our papers (BibTeX).

Sockeye 3.x

Felix Hieber, Michael Denkowski, Tobias Domhan, Barbara Darques Barros, Celina Dong Ye, Xing Niu, Cuong Hoang, Ke Tran, Benjamin Hsu, Maria Nadejde, Surafel Lakew, Prashant Mathur, Anna Currey, Marcello Federico. Sockeye 3: Fast Neural Machine Translation with PyTorch. ArXiv e-prints.

Sockeye 2.x

Tobias Domhan, Michael Denkowski, David Vilar, Xing Niu, Felix Hieber, Kenneth Heafield. The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020. Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (AMTA'20).

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar. Sockeye 2: A Toolkit for Neural Machine Translation. Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, Project Track (EAMT'20).

Sockeye 1.x

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, Matt Post. The Sockeye Neural Machine Translation Toolkit at AMTA 2018. Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (AMTA'18).

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post. 2017. Sockeye: A Toolkit for Neural Machine Translation. ArXiv e-prints.

Research with Sockeye

Sockeye has been used for both academic and industrial research. A list of known publications that use Sockeye is shown below. If you know more, please let us know or submit a pull request (last updated: May 2022).

2023

  • Zhang, Xuan, Kevin Duh, Paul McNamee. "A Hyperparameter Optimization Toolkit for Neural Machine Translation Research". Proceedings of ACL (2023).

2022

  • Currey, Anna, Maria Nădejde, Raghavendra Pappagari, Mia Mayer, Stanislas Lauly, Xing Niu, Benjamin Hsu, Georgiana Dinu. "MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation". Proceedings of EMNLP (2022).
  • Domhan, Tobias, Eva Hasler, Ke Tran, Sony Trenous, Bill Byrne and Felix Hieber. "The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation". Proceedings of NAACL-HLT (2022)
  • Fischer, Lukas, Patricia Scheurer, Raphael Schwitter, Martin Volk. "Machine Translation of 16th Century Letters from Latin to German". Workshop on Language Technologies for Historical and Ancient Languages (2022).
  • Knowles, Rebecca, Patrick Littell. "Translation Memories as Baselines for Low-Resource Machine Translation". Proceedings of LREC (2022)
  • McNamee, Paul, Kevin Duh. "The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text". Proceedings of LREC (2022)
  • Nadejde Maria, Anna Currey, Benjamin Hsu, Xing Niu, Marcello Federico, Georgiana Dinu. "CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality". Proceedings of NAACL (2022).
  • Weller-Di Marco, Marion, Matthias Huck, Alexander Fraser. "Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies ". arXiv preprint arXiv:2203.13550 (2022)

2021

  • Bergmanis, Toms, Mārcis Pinnis. "Facilitating Terminology Translation with Target Lemma Annotations". arXiv preprint arXiv:2101.10035 (2021)
  • Briakou, Eleftheria, Marine Carpuat. "Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation". arXiv preprint arXiv:2105.15087 (2021)
  • Hasler, Eva, Tobias Domhan, Sony Trenous, Ke Tran, Bill Byrne, Felix Hieber. "Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation". Proceedings of EMNLP (2021)
  • Tang, Gongbo, Philipp Rönchen, Rico Sennrich, Joakim Nivre. "Revisiting Negation in Neural Machine Translation". Transactions of the Association for Computation Linguistics 9 (2021)
  • Vu, Thuy, Alessandro Moschitti. "Machine Translation Customization via Automatic Training Data Selection from the Web". arXiv preprint arXiv:2102.1024 (2021)
  • Xu, Weijia, Marine Carpuat. "EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints." Transactions of the Association for Computation Linguistics 9 (2021)
  • Müller, Mathias, Rico Sennrich. "Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation". Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2021)
  • Popović, Maja, Alberto Poncelas. "On Machine Translation of User Reviews." Proceedings of RANLP (2021)
  • Popović, Maja. "On nature and causes of observed MT errors." Proceedings of the 18th MT Summit (Volume 1: Research Track) (2021)
  • Jain, Nishtha, Maja Popović, Declan Groves, Eva Vanmassenhove. "Generating Gender Augmented Data for NLP." Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing (2021)
  • Vilar, David, Marcello Federico. "A Statistical Extension of Byte-Pair Encoding." Proceedings of IWSLT (2021)

2020

  • Dinu, Georgiana, Prashant Mathur, Marcello Federico, Stanislas Lauly, Yaser Al-Onaizan. "Joint translation and unit conversion for end-to-end localization." Proceedings of IWSLT (2020)
  • Exel, Miriam, Bianka Buschbeck, Lauritz Brandt, Simona Doneva. "Terminology-Constrained Neural Machine Translation at SAP". Proceedings of EAMT (2020).
  • Hisamoto, Sorami, Matt Post, Kevin Duh. "Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?" Transactions of the Association for Computational Linguistics, Volume 8 (2020)
  • Naradowsky, Jason, Xuan Zhan, Kevin Duh. "Machine Translation System Selection from Bandit Feedback." arXiv preprint arXiv:2002.09646 (2020)
  • Niu, Xing, Prashant Mathur, Georgiana Dinu, Yaser Al-Onaizan. "Evaluating Robustness to Input Perturbations for Neural Machine Translation". arXiv preprint arXiv:2005.00580 (2020)
  • Niu, Xing, Marine Carpuat. "Controlling Neural Machine Translation Formality with Synthetic Supervision." Proceedings of AAAI (2020)
  • Keung, Phillip, Julian Salazar, Yichao Liu, Noah A. Smith. "Unsupervised Bitext Mining and Translation via Self-Trained Contextual Embeddings." arXiv preprint arXiv:2010.07761 (2020).
  • Sokolov, Alex, Tracy Rohlin, Ariya Rastrow. "Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion." arXiv preprint arXiv:2006.14194 (2020)
  • Stafanovičs, Artūrs, Toms Bergmanis, Mārcis Pinnis. "Mitigating Gender Bias in Machine Translation with Target Gender Annotations." arXiv preprint arXiv:2010.06203 (2020)
  • Stojanovski, Dario, Alexander Fraser. "Addressing Zero-Resource Domains Using Document-Level Context in Neural Machine Translation." arXiv preprint arXiv preprint arXiv:2004.14927 (2020)
  • Stojanovski, Dario, Benno Krojer, Denis Peskov, Alexander Fraser. "ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation". Proceedings of COLING (2020)
  • Zhang, Xuan, Kevin Duh. "Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems." Transactions of the Association for Computational Linguistics, Volume 8 (2020)
  • Swe Zin Moe, Ye Kyaw Thu, Hnin Aye Thant, Nandar Win Min, and Thepchai Supnithi, "Unsupervised Neural Machine Translation between Myanmar Sign Language and Myanmar Language", Journal of Intelligent Informatics and Smart Technology, April 1st Issue, 2020, pp. 53-61. (Submitted December 21, 2019; accepted March 6, 2020; revised March 16, 2020; published online April 30, 2020)
  • Thazin Myint Oo, Ye Kyaw Thu, Khin Mar Soe and Thepchai Supnithi, "Neural Machine Translation between Myanmar (Burmese) and Dawei (Tavoyan)", In Proceedings of the 18th International Conference on Computer Applications (ICCA 2020), Feb 27-28, 2020, Yangon, Myanmar, pp. 219-227
  • Müller, Mathias, Annette Rios, Rico Sennrich. "Domain Robustness in Neural Machine Translation." Proceedings of AMTA (2020)
  • Rios, Annette, Mathias Müller, Rico Sennrich. "Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation." Proceedings of the 5th WMT: Research Papers (2020)
  • Popović, Maja, Alberto Poncelas. "Neural Machine Translation between similar South-Slavic languages." Proceedings of the 5th WMT: Research Papers (2020)
  • Popović, Maja, Alberto Poncelas. "Extracting correctly aligned segments from unclean parallel data using character n-gram matching." Proceedings of Conference on Language Technologies & Digital Humanities (JTDH 2020).
  • Popović, Maja, Alberto Poncelas, Marija Brkic, Andy Way. "Neural Machine Translation for translating into Croatian and Serbian." Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects (2020)

2019

  • Agrawal, Sweta, Marine Carpuat. "Controlling Text Complexity in Neural Machine Translation." Proceedings of EMNLP (2019)
  • Beck, Daniel, Trevor Cohn, Gholamreza Haffari. "Neural Speech Translation using Lattice Transformations and Graph Networks." Proceedings of TextGraphs-13 (EMNLP 2019)
  • Currey, Anna, Kenneth Heafield. "Zero-Resource Neural Machine Translation with Monolingual Pivot Data." Proceedings of EMNLP (2019)
  • Gupta, Prabhakar, Mayank Sharma. "Unsupervised Translation Quality Estimation for Digital Entertainment Content Subtitles." IEEE International Journal of Semantic Computing (2019)
  • Hu, J. Edward, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, and Benjamin Van Durme. "Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting." Proceedings of NAACL-HLT (2019)
  • Rosendahl, Jan, Christian Herold, Yunsu Kim, Miguel Graça,Weiyue Wang, Parnia Bahar, Yingbo Gao and Hermann Ney “The RWTH Aachen University Machine Translation Systems for WMT 2019” Proceedings of the 4th WMT: Research Papers (2019)
  • Thompson, Brian, Jeremy Gwinnup, Huda Khayrallah, Kevin Duh, and Philipp Koehn. "Overcoming catastrophic forgetting during domain adaptation of neural machine translation." Proceedings of NAACL-HLT 2019 (2019)
  • Tättar, Andre, Elizaveta Korotkova, Mark Fishel “University of Tartu’s Multilingual Multi-domain WMT19 News Translation Shared Task Submission” Proceedings of 4th WMT: Research Papers (2019)
  • Thazin Myint Oo, Ye Kyaw Thu and Khin Mar Soe, "Neural Machine Translation between Myanmar (Burmese) and Rakhine (Arakanese)", In Proceedings of the Sixth Workshop on NLP for Similar Languages, Varieties and Dialects, NAACL-2019, June 7th 2019, Minneapolis, United States, pp. 80-88

2018

  • Domhan, Tobias. "How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures". Proceedings of 56th ACL (2018)
  • Kim, Yunsu, Yingbo Gao, and Hermann Ney. "Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies." arXiv preprint arXiv:1905.05475 (2019)
  • Korotkova, Elizaveta, Maksym Del, and Mark Fishel. "Monolingual and Cross-lingual Zero-shot Style Transfer." arXiv preprint arXiv:1808.00179 (2018)
  • Niu, Xing, Michael Denkowski, and Marine Carpuat. "Bi-directional neural machine translation with synthetic parallel data." arXiv preprint arXiv:1805.11213 (2018)
  • Niu, Xing, Sudha Rao, and Marine Carpuat. "Multi-Task Neural Models for Translating Between Styles Within and Across Languages." COLING (2018)
  • Post, Matt and David Vilar. "Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation." Proceedings of NAACL-HLT (2018)
  • Schamper, Julian, Jan Rosendahl, Parnia Bahar, Yunsu Kim, Arne Nix, and Hermann Ney. "The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018." Proceedings of the 3rd WMT: Shared Task Papers (2018)
  • Schulz, Philip, Wilker Aziz, and Trevor Cohn. "A stochastic decoder for neural machine translation." arXiv preprint arXiv:1805.10844 (2018)
  • Tamer, Alkouli, Gabriel Bretschner, and Hermann Ney. "On The Alignment Problem In Multi-Head Attention-Based Neural Machine Translation." Proceedings of the 3rd WMT: Research Papers (2018)
  • Tang, Gongbo, Rico Sennrich, and Joakim Nivre. "An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation." Proceedings of 3rd WMT: Research Papers (2018)
  • Thompson, Brian, Huda Khayrallah, Antonios Anastasopoulos, Arya McCarthy, Kevin Duh, Rebecca Marvin, Paul McNamee, Jeremy Gwinnup, Tim Anderson, and Philipp Koehn. "Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation." arXiv preprint arXiv:1809.05218 (2018)
  • Vilar, David. "Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models." Proceedings of NAACL-HLT (2018)
  • Vyas, Yogarshi, Xing Niu and Marine Carpuat “Identifying Semantic Divergences in Parallel Text without Annotations”. Proceedings of NAACL-HLT (2018)
  • Wang, Weiyue, Derui Zhu, Tamer Alkhouli, Zixuan Gan, and Hermann Ney. "Neural Hidden Markov Model for Machine Translation". Proceedings of 56th ACL (2018)
  • Zhang, Xuan, Gaurav Kumar, Huda Khayrallah, Kenton Murray, Jeremy Gwinnup, Marianna J Martindale, Paul McNamee, Kevin Duh, and Marine Carpuat. "An Empirical Exploration of Curriculum Learning for Neural Machine Translation." arXiv preprint arXiv:1811.00739 (2018)
  • Swe Zin Moe, Ye Kyaw Thu, Hnin Aye Thant and Nandar Win Min, "Neural Machine Translation between Myanmar Sign Language and Myanmar Written Text", In the second Regional Conference on Optical character recognition and Natural language processing technologies for ASEAN languages 2018 (ONA 2018), December 13-14, 2018, Phnom Penh, Cambodia.
  • Tang, Gongbo, Mathias Müller, Annette Rios and Rico Sennrich. "Why Self-attention? A Targeted Evaluation of Neural Machine Translation Architectures." Proceedings of EMNLP (2018)

2017

  • Domhan, Tobias and Felix Hieber. "Using target-side monolingual data for neural machine translation through multi-task learning." Proceedings of EMNLP (2017).