Although large language models (LLMs) have shown surprising language understanding and generation capabilities, they have yet to gain a revolutionary advancement in the field of machine translation. One potential cause of the limited performance is the misalignment between the translation-specific understanding and general understanding inside LLMs. To align the translation-specific understanding to the general one, we propose a novel translation process xIoD (Cross-Lingual Interpretation of Difficult words), explicitly incorporating the general understanding on the content incurring inconsistent understanding to guide the translation. Specifically, xIoD performs the cross-lingual interpretation for the difficult-to-translate words and enhances the translation with the generated interpretations. Furthermore, we reframe the external tools of QE to tackle the challenges of xIoD in the detection of difficult words and the generation of helpful interpretations. We conduct experiments on the self-constructed benchmark ChallengeMT, which includes cases in which multiple SOTA translation systems consistently underperform. Experimental results show the effectiveness of our xIoD, which improves up to +3.85 COMET.

翻译：尽管大型语言模型（LLMs）在语言理解和生成方面展现出令人惊讶的能力，但它们在机器翻译领域尚未实现革命性进展。性能受限的一个潜在原因在于LLMs内部翻译特定理解与通用理解之间的错位。为了将翻译特定理解与通用理解对齐，我们提出了一种新颖的翻译流程xIoD（跨语言难词理解），该流程明确地将对导致理解不一致内容的通用理解融入以指导翻译。具体而言，xIoD对难以翻译的词汇进行跨语言解释，并利用生成的解释增强翻译质量。此外，我们重新构想了QE（质量评估）的外部工具，以应对xIoD在难词检测和生成有效解释方面的挑战。我们在自行构建的基准测试集ChallengeMT上进行了实验，该基准包含多个SOTA翻译系统持续表现不佳的案例。实验结果表明，我们的xIoD方法有效，COMET评分最多提升+3.85分。