Since the first online demonstration of Neural Machine Translation (NMT) by
LISA, NMT development has recently moved from laboratory to production systems
as demonstrated by several entities announcing roll-out of NMT engines to
replace their existing technologies. NMT systems have a large number of
training configurations and the training process of such systems is usually
very long, often a few weeks, so role of experimentation is critical and
important to share. In this work, we present our approach to production-ready
systems simultaneously with release of online demonstrators covering a large
variety of languages (12 languages, for 32 language pairs). We explore
different practical choices: an efficient and evolutive open-source framework;
data preparation; network architecture; additional implemented features; tuning
for production; etc. We discuss about evaluation methodology, present our first
findings and we finally outline further work.
Our ultimate goal is to share our expertise to build competitive production
systems for "generic" translation. We aim at contributing to set up a
collaborative framework to speed-up adoption of the technology, foster further
research efforts and enable the delivery and adoption to/by industry of
use-case specific engines integrated in real production workflows. Mastering of
the technology would allow us to build translation engines suited for
particular needs, outperforming current simplest/uniform systems.

本研究旨在分享我们在神经机器翻译领域的专业知识，创造出竞争力强且适用于不同用例的翻译引擎，在实际生产流程中实现特定领域的高效翻译。我们提供了一种同时发布多种语言在线演示程序的方法，并探索了不同的实用选项、网络架构和评估方法等。