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.

Sockeye 是一个用于神经机器翻译的开放源代码序列到序列工具包，它支持三种最流行的编码器 - 解码器体系结构，包括注意力循环神经网络、自我关注变换器和全卷积网络； Sockeye 在英语 - 德语和拉脱维亚语 - 英语两种语言翻译任务中获得了竞争力的 BLEU 分数，特别是其变压器实现的总体最佳分数。