Abstract
Conventional retrieval-augmented neural machine translation (RANMT) systems leverage bilingual corpora, e.g., translation memories (TMs). Yet, in many settings, in-domain monolingual target-side corpora are often available. This work explores ways to take advantage of such resources by retrieving relevant segments directly in the target language, based on a source-side query. For this, we design improved cross-lingual retrieval systems, trained with both sentence level and word-level matching objectives. In our experiments with two RANMT architectures, we first demonstrate the benefits of such cross-lingual objectives in a controlled setting, obtaining translation performances that surpass standard TM-based models. We then showcase our method on a real-world set-up, where the target monolingual resources far exceed the amount of parallel data and observe large improvements of our new techniques, which outperform both the baseline setting, and general-purpose cross-lingual retrievers.
Abstract (translated)
传统的检索增强神经机器翻译（RANMT）系统依赖于双语语料库，例如翻译记忆库（TMs）。然而，在许多情况下，目标语言中的单语领域内语料库通常是可以获取的。本研究探索了利用这些资源的方法，通过在目标语言中直接检索与源语言查询相关的段落来实现这一目标。为此，我们设计了改进的跨语言检索系统，并使用句子级别和词汇级别的匹配目标进行训练。 在两个RANMT架构上的实验中，我们在一个控制环境中首先展示了此类跨语言目标的优势，获得了超过标准TM基线模型的翻译性能。然后，我们在一个现实世界的设置下展示我们的方法，在该设置中目标单语资源远远超过了并行数据的数量，并观察到我们新技术带来的显著改进，这些技术在性能上超越了基线设置和通用型跨语言检索器。
URL
https://arxiv.org/abs/2504.21747
