The OpenNMT Neural Machine Translation Toolkit: 2020 Edition

OpenNMT is a multi-year open-source ecosystem for neural machine translation (NMT) and natural language generation (NLG). The toolkit consists of multiple projects to cover the complete machine learning workflow: from data preparation to inference acceleration. The systems prioritize efficiency, modularity, and extensibility with the goal of supporting research into model architectures, feature representations, and source modalities, while maintaining API stability and competitive performance for production usages. OpenNMT has been used in several production MT systems and cited in more than 700 research papers.

[1]  Pavel Levin,et al.  Machine Translation at Booking.com: Journey and Lessons Learned , 2017, ArXiv.

[2]  Christopher D. Manning,et al.  Get To The Point: Summarization with Pointer-Generator Networks , 2017, ACL.

[3]  Rico Sennrich,et al.  Neural Machine Translation of Rare Words with Subword Units , 2015, ACL.

[4]  Alexander M. Rush,et al.  Image-to-Markup Generation with Coarse-to-Fine Attention , 2016, ICML.

[5]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[6]  Alexander M. Rush,et al.  Challenges in Data-to-Document Generation , 2017, EMNLP.

[7]  Yann Dauphin,et al.  Convolutional Sequence to Sequence Learning , 2017, ICML.

[8]  Jindrich Libovický,et al.  Input Combination Strategies for Multi-Source Transformer Decoder , 2018, WMT.

[9]  Idriss Mghabbar,et al.  Building a Multi-domain Neural Machine Translation Model using Knowledge Distillation , 2020, ECAI.

[10]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[11]  Bo Wang,et al.  SYSTRAN's Pure Neural Machine Translation Systems , 2016, ArXiv.

[12]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[13]  Alexander M. Rush,et al.  Bottom-Up Abstractive Summarization , 2018, EMNLP.

[14]  Marta R. Costa-jussà,et al.  Findings of the 2019 Conference on Machine Translation (WMT19) , 2019, WMT.

[15]  Ankur Bapna,et al.  The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation , 2018, ACL.

[16]  Pierrette Bouillon,et al.  Preferences of end-users for raw and post-edited NMT in a business environment , 2019 .

[17]  André F. T. Martins,et al.  Marian: Fast Neural Machine Translation in C++ , 2018, ACL.

[18]  Johan Bos,et al.  Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations , 2017, ArXiv.

[19]  Myle Ott,et al.  fairseq: A Fast, Extensible Toolkit for Sequence Modeling , 2019, NAACL.

[20]  Alexander M. Rush,et al.  OpenNMT: Open-Source Toolkit for Neural Machine Translation , 2017, ACL.

[21]  Matt Post,et al.  We start by defining the recurrent architecture as implemented in S OCKEYE , following , 2018 .

[22]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[23]  Taku Kudo,et al.  SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing , 2018, EMNLP.

[24]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[25]  George Kurian,et al.  Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.