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.

翻译：近年来，机器翻译技术取得了重大进展，但仍无法保证零错误结果。在计算机辅助翻译场景中，人工译员对机器翻译结果进行后期编辑以修正错误。为加速后期编辑流程，许多研究探索了交互式机器翻译模式，该模式下机器可根据人工编辑的约束自动优化剩余译文的翻译。翻译建议（Translation Suggestion, TS）作为一种辅助人工译员的交互模式，要求机器针对人工译员选定的特定错误词或短语生成替换方案。本文利用神经机器翻译（NMT）的参数化目标函数，提出了一种新型约束解码算法——前后缀引导解码（Prefix Suffix Guided Decoding, PSGD），无需额外训练即可解决TS问题。与最先进的词汇约束解码方法相比，PSGD在WeTS和WMT 2022翻译建议数据集上分别将翻译质量平均提升$10.87$ BLEU和$8.62$ BLEU，并在WMT翻译数据集测试中将解码时间开销平均降低63.4%。此外，在两个TS基准数据集上，该方法的性能均优于使用TS标注数据训练的监督学习系统。