赋予旧方法新活力：质量估计辅助的约束解码用于自动后编辑
Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing
AI总结：
本文提出一种架构无关的词级质量估计辅助约束解码方法，通过在解码时引入QE信息缓解自动后编辑系统的过度纠正问题，在多语言对上显著提升了TER指标。
摘要：
自动后编辑（APE）系统经常面临过度纠正的难题，即对翻译进行不必要的修改，偏离了最小编辑原则。在本文中，我们提出了一种新技术，通过在解码过程中引入词级质量估计（QE）信息来缓解过度纠正。该方法与架构无关，可适配任何APE系统，无论其底层模型或训练方法如何。我们在英德、英印地语和英马拉地语语言对上的实验表明，所提方法相比相应的基线APE系统取得了显著提升，TER分别改善了0.65、1.86和1.44个点。这些结果强调了QE与APE任务之间的互补关系，并突显了整合QE信息以减少APE系统中过度纠正的有效性。
英文摘要：
Automatic Post-Editing (APE) systems often struggle with over-correction, where unnecessary modifications are made to a translation, diverging from the principle of minimal editing. In this paper, we propose a novel technique to mitigate over-correction by incorporating word-level Quality Estimation (QE) information during the decoding process. This method is architecture-agnostic, making it adaptable to any APE system, regardless of the underlying model or training approach. Our experiments on English-German, English-Hindi, and English-Marathi language pairs show the proposed approach yields significant improvements over their corresponding baseline APE systems, with TER gains of $0.65$, $1.86$, and $1.44$ points, respectively. These results underscore the complementary relationship between QE and APE tasks and highlight the effectiveness of integrating QE information to reduce over-correction in APE systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
