Insufficient modeling of human preferences within the reward model is a major obstacle for leveraging human feedback to improve translation quality. Fortunately, quality estimation (QE), which predicts the quality of a given translation without reference, has achieved impressive alignment with human evaluations in the last two years. In this work, we investigate the potential of employing the QE model as the reward model (the QE-based reward model) to predict human preferences for feedback training. We first identify the overoptimization problem during QE-based feedback training, manifested as an increase in reward while translation quality declines. We examine the problem and argue that the vulnerability of the QE model might lead to high rewards for incorrect translations, resulting in overoptimization and error propagation. To address the problem, we adopt a simple yet effective method that uses heuristic rules to detect the incorrect translations and assigns a penalty term to the QE-based rewards for the detected incorrect translations. Experimental results show that the proposed QE-based feedback training achieves consistent and significant improvements across various settings, further verified through human preference studies. Our subsequent analysis demonstrates the high data efficiency of the proposed QE-based feedback training: the proposed approach using a small amount of monolingual data can outperform systems using larger parallel corpora.

翻译：奖励模型中对人类偏好建模不足是利用人类反馈提升翻译质量的主要障碍。幸运的是，质量估计（QE）技术无需参考译文即可预测给定翻译的质量，在过去两年中已实现与人类评估的高度一致性。本研究探讨了将QE模型作为奖励模型（基于QE的奖励模型）用于预测人类偏好以进行反馈训练的潜力。我们首先发现基于QE的反馈训练存在过度优化问题，表现为奖励增加而翻译质量下降。通过分析该问题，我们认为QE模型的脆弱性可能导致错误翻译获得过高奖励，从而引发过度优化和错误传播。为解决这一问题，我们采用了一种简单有效的方法：使用启发式规则检测错误翻译，并对检测出的错误翻译的基于QE的奖励施加惩罚项。实验结果表明，所提出的基于QE的反馈训练方案在不同设置下均取得了一致且显著的改进，并通过人类偏好研究进一步验证。后续分析证明了该方案的高数据效率：使用少量单语数据的本方法可超越使用更大规模平行语料库的系统。