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 autosuggestion 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 autocompletions.