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。

基于单词级别自动完成：开箱即用我们能取得什么成果？

Translation Word-Level Auto-Completion: What can we achieve out of the box?

Generating clinical reports from raw recordings such as X-rays and
electroencephalogram (EEG) is an essential and routine task for doctors.
However, it is often time-consuming to write accurate and detailed reports.
Most existing methods try to generate the whole reports from the raw input with
limited success because 1) generated reports often contain errors that need
manual review and correction, 2) it does not save time when doctors want to
write additional information into the report, and 3) the generated reports are
not customized based on individual doctors' preference. We propose {\it
CL}inic{\it A}l {\it R}eport {\it A}uto-completion (CLARA), an interactive
method that generates reports in a sentence by sentence fashion based on
doctors' anchor words and partially completed sentences. CLARA searches for
most relevant sentences from existing reports as the template for the current
report. The retrieved sentences are sequentially modified by combining with the
input feature representations to create the final report. In our experimental
evaluation, CLARA achieved 0.393 CIDEr and 0.248 BLEU-4 on X-ray reports and
0.482 CIDEr and 0.491 BLEU-4 for EEG reports for sentence-level generation,
which is up to 35% improvement over the best baseline. Also via our qualitative
evaluation, CLARA is shown to produce reports which have a significantly higher
level of approval by doctors in a user study (3.74 out of 5 for CLARA vs 2.52
out of 5 for the baseline).

CLARA 是一种交互式方法，可基于医生的关键词和部分完成的句子逐句生成报告，其中检索的句子会与输入功能表示相结合进行修改以创建最终报告，并在句子级别上实现了 35％的改进，其在医生用户研究中产生的报告数量显着高于基线。