  Machine translation technology has made great progress in recent years, but
it cannot guarantee error free results. Human translators perform post editing
on machine translations to correct errors in the scene of computer aided
translation. In favor of expediting the post editing process, many works have
investigated machine translation in interactive modes, in which machines can
automatically refine the rest of translations constrained by human's edits.
Translation Suggestion (TS), as an interactive mode to assist human
translators, requires machines to generate alternatives for specific incorrect
words or phrases selected by human translators. In this paper, we utilize the
parameterized objective function of neural machine translation (NMT) and
propose a novel constrained decoding algorithm, namely Prefix Suffix Guided
Decoding (PSGD), to deal with the TS problem without additional training.
Compared to the state of the art lexically constrained decoding method, PSGD
improves translation quality by an average of $10.87$ BLEU and $8.62$ BLEU on
the WeTS and the WMT 2022 Translation Suggestion datasets, respectively, and
reduces decoding time overhead by an average of 63.4% tested on the WMT
translation datasets. Furthermore, on both of the TS benchmark datasets, it is
superior to other supervised learning systems trained with TS annotated data.
