论文标题
通过控制源和模糊匹配互动来改善检索增强神经机的翻译
Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions
论文作者
论文摘要
我们探索零射击适应，其中通用域模型可以在推理时访问客户或域特定的并行数据，而在培训期间则不能访问特定的并行数据。我们建立在检索增强翻译（大鼠）的想法的基础上，其中找到了源句子的TOP-K内模糊匹配项，并且在推理时，将这些模糊匹配的句子的目标翻译提供给翻译模型。我们提出了一种新颖的体系结构，以控制源句子与顶级模糊目标语言匹配之间的相互作用，并将其与先前工作的体系结构进行比较。我们通过在WMT数据上培训模型并分别使用五个和七个多域数据集对它们进行两种语言对（EN-DE和EN-FR）进行实验。我们的方法始终优于替代体系结构，在语言对，域和模糊匹配的数字K中改善了BLEU。
We explore zero-shot adaptation, where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training. We build on the idea of Retrieval Augmented Translation (RAT) where top-k in-domain fuzzy matches are found for the source sentence, and target-language translations of those fuzzy-matched sentences are provided to the translation model at inference time. We propose a novel architecture to control interactions between a source sentence and the top-k fuzzy target-language matches, and compare it to architectures from prior work. We conduct experiments in two language pairs (En-De and En-Fr) by training models on WMT data and testing them with five and seven multi-domain datasets, respectively. Our approach consistently outperforms the alternative architectures, improving BLEU across language pair, domain, and number k of fuzzy matches.
