In this paper, we address the data scarcity problem in automatic data-driven
glossing for low-resource languages by coordinating multiple sources of
linguistic expertise. We supplement models with translations at both the token
and sentence level as well as leverage the extensive linguistic capability of
modern LLMs. Our enhancements lead to an average absolute improvement of
5%-points in word-level accuracy over the previous state of the art on a
typologically diverse dataset spanning six low-resource languages. The
improvements are particularly noticeable for the lowest-resourced language
Gitksan, where we achieve a 10%-point improvement. Furthermore, in a simulated
ultra-low resource setting for the same six languages, training on fewer than
100 glossed sentences, we establish an average 10%-point improvement in
word-level accuracy over the previous state-of-the-art system.

通过协调多个语言专业知识源，我们解决了低资源语言自动数据驱动预览中的数据稀缺问题，并在词级准确性方面获得了 5 个百分点的平均绝对改进，对横跨六种低资源语言的多样化数据集而言，这些增强在最低资源的 Gitksan 语言中表现尤为明显，我们实现了 10 个百分点的改进。此外，在相同六种语言的模拟超低资源环境中，仅在少于 100 个含义标注句子上训练下，我们在词级准确性方面获得了 10 个百分点的平均改进，超越了以往最先进系统的水平。