Sign languages, often categorised as low-resource languages, face significant
challenges in achieving accurate translation due to the scarcity of parallel
annotated datasets. This paper introduces Select and Reorder (S&R), a novel
approach that addresses data scarcity by breaking down the translation process
into two distinct steps: Gloss Selection (GS) and Gloss Reordering (GR). Our
method leverages large spoken language models and the substantial lexical
overlap between source spoken languages and target sign languages to establish
an initial alignment. Both steps make use of Non-AutoRegressive (NAR) decoding
for reduced computation and faster inference speeds. Through this
disentanglement of tasks, we achieve state-of-the-art BLEU and Rouge scores on
the Meine DGS Annotated (mDGS) dataset, demonstrating a substantial BLUE-1
improvement of 37.88% in Text to Gloss (T2G) Translation. This innovative
approach paves the way for more effective translation models for sign
languages, even in resource-constrained settings.

该论文提出了一种名为选择和重新排序（S&R）的新方法，通过将翻译过程分为两个不同的步骤：字面选择（GS）和字面重新排序（GR），来解决低资源语言 —— 手语因平行注释数据稀缺而实现准确翻译面临的挑战。此方法利用大型口语语言模型和源口语语言与目标手语之间的大量词汇重叠建立初始对齐。通过任务解耦，我们在 Meine DGS Annotated (mDGS) 数据集上实现了最新颖的 BLEU 和 Rouge 得分，并在文本到字面（T2G）翻译中实现了 37.88% 的 BLUE-1 改进。这种创新性的方法为手语的更有效翻译模型铺平了道路，甚至在资源有限的情况下。