  Computer-aided translation (CAT), the use of software to assist a human
translator in the translation process, has been proven to be useful in
enhancing the productivity of human translators. Autocompletion, which suggests
translation results according to the text pieces provided by human translators,
is a core function of CAT. There are two limitations in previous research in
this line. First, most research works on this topic focus on sentence-level
autocompletion (i.e., generating the whole translation as a sentence based on
human input), but word-level autocompletion is under-explored so far. Second,
almost no public benchmarks are available for the autocompletion task of CAT.
This might be among the reasons why research progress in CAT is much slower
compared to automatic MT. In this paper, we propose the task of general
word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct
the first public benchmark to facilitate research in this topic. In addition,
we propose an effective method for GWLAN and compare it with several strong
baselines. Experiments demonstrate that our proposed method can give
significantly more accurate predictions than the baseline methods on our
benchmark datasets.
