Terminology correctness is important in the downstream application of machine
translation, and a prevalent way to ensure this is to inject terminology
constraints into a translation system. In our submission to the WMT 2023
terminology translation task, we adopt a translate-then-refine approach which
can be domain-independent and requires minimal manual efforts. We annotate
random source words with pseudo-terminology translations obtained from word
alignment to first train a terminology-aware model. Further, we explore two
post-processing methods. First, we use an alignment process to discover whether
a terminology constraint has been violated, and if so, we re-decode with the
violating word negatively constrained. Alternatively, we leverage a large
language model to refine a hypothesis by providing it with terminology
constraints. Results show that our terminology-aware model learns to
incorporate terminologies effectively, and the large language model refinement
process can further improve terminology recall.

在机器翻译的下游应用中，术语的正确性非常重要，通过将术语约束注入翻译系统可以实现。本研究采用一种翻译后再优化的方法，可以实现跨领域且需要较少手动操作。我们通过使用从词对齐中获得的伪术语翻译来注释随机源词，首先训练一个术语感知模型。此外，我们还探索了两种后处理方法。第一，我们使用对齐过程来发现是否违反了术语约束，如果是，则使用负面约束对违反术语的词进行重新解码。或者，我们利用大型语言模型通过提供术语约束来优化假设。结果表明，我们的术语感知模型能够有效地学习并整合术语，而大型语言模型优化过程可以进一步提高术语的召回率。