Abstract
In real world translation scenarios, terminology is rarely one-to-one. Instead, multiple valid translations may appear in a terminology dictionary, but correctness of a translation depends on corporate style guides and context. This can be challenging for neural machine translation (NMT) systems. Luckily, in a corporate context, many examples of human post-edits of valid but incorrect terminology exist. The goal of this work is to learn how to disambiguate our terminology based on these corrections. Our approach is based on preference optimization, using the term post-edit as the knowledge to be preferred. While previous work had to rely on unambiguous translation dictionaries to set hard constraints during decoding, or to add soft constraints in the input, our framework requires neither one-to-one dictionaries nor human intervention at decoding time. We report results on English-German post-edited data and find that the optimal combination of supervised fine-tuning and preference optimization, with both term-specific and full sequence objectives, yields statistically significant improvements in term accuracy over a strong NMT baseline without significant losses in COMET score. Additionally, we release test sets from our post-edited data and terminology dictionary.
Abstract (translated)
在真实的翻译场景中，术语的对应关系很少是一对一的。相反，在术语词典中可能会存在多个有效的译文选项，但正确的翻译取决于公司的风格指南和具体上下文环境。这对神经机器翻译（NMT）系统来说是一个挑战。幸运的是，在企业环境中，有许多人类后编辑人员修正不正确术语的实际例子可用。 这项工作的目标是基于这些校正示例学习如何对术语进行语境化区别。我们的方法基于偏好优化，以术语的后编辑结果作为优先选择的知识。以往的研究工作不得不依赖于无歧义的翻译词典来在解码阶段设置硬性约束条件，或者在输入中添加软性约束条件。而我们提出的方法既不需要一对一的词典也不需要在解码时进行人工干预。 我们在英语-德语后编辑数据上报告了实验结果，并发现最优结合监督微调和偏好优化（同时使用术语特定目标和完整序列目标）的方法，相比于一个强大的NMT基线，在术语准确性上有统计显著性的改进，而不会导致COMET评分有明显下降。此外，我们还发布了从我们的后编辑数据集中提取的测试集以及相关的术语词典。
URL
https://arxiv.org/abs/2507.03580
