论文标题
轻松指导解码为交互式机器翻译提供建议
Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation
论文作者
论文摘要
近年来，机器翻译技术取得了长足的进步，但不能保证无错误的结果。人类翻译人员对机器翻译进行邮政编辑，以纠正计算机辅助翻译场景中的错误。为了加快邮政编辑过程的加快，许多作品都以交互式模式调查了机器翻译，其中机器可以自动完善受人类编辑约束的其余翻译。翻译建议（TS）是一种交互式模式来协助人类翻译人员，需要机器为人类翻译人员选择的特定错误单词或短语生成替代方案。在本文中，我们利用神经机器翻译（NMT）的参数化目标函数，并提出了一种新颖的约束解码算法，即前缀后缀指导的解码（PSGD），无需额外的培训即可处理TS问题。与词汇限制的解码方法相比，PSGD平均将翻译质量提高了$ 10.87 $ bleu和$ 8.62 $ bleu的潮湿和wmt 2022翻译建议数据集，并将平均在WMT翻译的63.4％测试的时间范围内减少63.4％的测试。此外，在两个TS基准数据集中，它都优于其他受TS注释数据训练的监督学习系统。
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.
