Pronoun translation is a longstanding challenge in neural machine translation (NMT), often requiring inter-sentential context to ensure linguistic accuracy. To address this, we introduce ProNMT, a novel framework designed to enhance pronoun and overall translation quality in context-aware machine translation systems. ProNMT leverages Quality Estimation (QE) models and a unique Pronoun Generation Likelihood-Based Feedback mechanism to iteratively fine-tune pre-trained NMT models without relying on extensive human annotations. The framework combines QE scores with pronoun-specific rewards to guide training, ensuring improved handling of linguistic nuances. Extensive experiments demonstrate significant gains in pronoun translation accuracy and general translation quality across multiple metrics. ProNMT offers an efficient, scalable, and context-aware approach to improving NMT systems, particularly in translating context-dependent elements like pronouns.

本研究针对神经机器翻译中代词翻译的挑战，引入了一种新颖的ProNMT框架，通过质量评估模型和代词生成基于概率的反馈机制，优化了训练过程。实验结果表明，该框架显著提升了代词翻译的准确性及整体翻译质量，具有高效、可扩展的优势。

基于质量评估的反馈训练以改善代词翻译