ViNMT: Neural Machine Translation Toolkit
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Abstract (arXiv)
We present an open-source toolkit for neural machine translation (NMT). The new toolkit is mainly based on vaulted Transformer (Vaswani et al., 2017) along with many other improvements detailed below, in order to create a self-contained, simple to use, consistent and comprehensive framework for Machine Translation tasks of various domains. It is tooled to support both bilingual and multilingual translation tasks, starting from building the model from respective corpora, to inferring new predictions or packaging the model to serving-capable JIT format.
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References · 14
- 2017Attention Is All You Needcited by 60,361
- 2020Language Models are Few-Shot Learnerscited by 32,419
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- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,488
- 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
- 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 469
- 2019Massively Multilingual Neural Machine Translationcited by 246
- 2018Marian: Fast Neural Machine Translation in C++cited by 203
- 2016Toward Multilingual Neural Machine Translation with Universal Encoder and Decodercited by 76
- 2019Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlationscited by 61
2 more
- 2018OpenNMT: Neural Machine Translation Toolkitcited by 27
- 2021OmniNet: Omnidirectional Representations from Transformerscited by 23
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