# Sockeye: A Toolkit for Neural Machine Translation

arXiv: https://arxiv.org/abs/1712.05690 · HTML: https://mlsanity.com/paper/1712.05690/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2017.

## Built on by (most cited first, 15 of 15)

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- [HC4: A New Suite of Test Collections for Ad Hoc CLIR](https://mlsanity.com/paper/2201.09992/) (2022)
- [Extremely low-resource machine translation for closely related languages](https://mlsanity.com/paper/2105.13065/) (2021)
- [Revisiting Negation in Neural Machine Translation](https://mlsanity.com/paper/2107.12203/) (2021)
- [Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22](https://mlsanity.com/paper/2212.01140/) (2022)
- [Machine Translation Customization via Automatic Training Data Selection from the Web](https://mlsanity.com/paper/2102.10243/) (2021)
- [Sicilian Translator: A Recipe for Low-Resource NMT](https://mlsanity.com/paper/2110.01938/) (2021)
- [Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies](https://mlsanity.com/paper/2203.13550/) (2022)
- [Improving Zero-shot Multilingual Neural Machine Translation for Low-Resource Languages](https://mlsanity.com/paper/2110.00712/) (2021)
- [SignBank+: Preparing a Multilingual Sign Language Dataset for Machine Translation Using Large Language Models](https://mlsanity.com/paper/2309.11566/) (2023)
- [Data Kernel Perspective Space Performance Guarantees for Synthetic Data from Transformer Models](https://mlsanity.com/paper/2602.05106/) (2026)

## Abstract (arXiv)

We describe Sockeye (version 1.12), an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNet, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks. Sockeye also supports a wide range of optimizers, normalization and regularization techniques, and inference improvements from current NMT literature. Users can easily run standard training recipes, explore different model settings, and incorporate new ideas. In this paper, we highlight Sockeye's features and benchmark it against other NMT toolkits on two language arcs from the 2017 Conference on Machine Translation (WMT): English-German and Latvian-English. We report competitive BLEU scores across all three architectures, including an overall best score for Sockeye's transformer implementation. To facilitate further comparison, we release all system outputs and training scripts used in our experiments. The Sockeye toolkit is free software released under the Apache 2.0 license.