Research on Machine Translation (MT) has achieved important breakthroughs in
several areas. While there is much more to be done in order to build on this
success, we believe that the language industry needs better ways to take full
advantage of current achievements. Due to a combination of factors, including
time, resources, and skills, businesses tend to apply pragmatism into their AI
workflows. Hence, they concentrate more on outcomes, e.g. delivery, shipping,
releases, and features, and adopt high-level working production solutions,
where possible. Among the features thought to be helpful for translators are
sentence-level and word-level translation auto-suggestion and auto-completion.
Suggesting alternatives can inspire translators and limit their need to refer
to external resources, which hopefully boosts their productivity. This work
describes our submissions to WMT's shared task on word-level auto-completion,
for the Chinese-to-English, English-to-Chinese, German-to-English, and
English-to-German language directions. We investigate the possibility of using
pre-trained models and out-of-the-box features from available libraries. We
employ random sampling to generate diverse alternatives, which reveals good
results. Furthermore, we introduce our open-source API, based on CTranslate2,
to serve translations, auto-suggestions, and auto-completions.

本研究利用预训练模型和随机抽样等方法，为机器翻译提供了句子和单词级别的自动翻译建议和自动补全功能，并且介绍了一种基于 CTranslate2 的翻译 API。