  Computer-aided translation (CAT) aims to enhance human translation efficiency
and is still important in scenarios where machine translation cannot meet
quality requirements. One fundamental task within this field is Word-Level Auto
Completion (WLAC). WLAC predicts a target word given a source sentence,
translation context, and a human typed character sequence. Previous works
either employ word classification models to exploit contextual information from
both sides of the target word or directly disregarded the dependencies from the
right-side context. Furthermore, the key information, i.e. human typed
sequences, is only used as prefix constraints in the decoding module. In this
paper, we propose the INarIG (Iterative Non-autoregressive Instruct Generation)
model, which constructs the human typed sequence into Instruction Unit and
employs iterative decoding with subwords to fully utilize input information
given in the task. Our model is more competent in dealing with low-frequency
words (core scenario of this task), and achieves state-of-the-art results on
the WMT22 and benchmark datasets, with a maximum increase of over 10%
prediction accuracy.
