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        <title>Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing</title>
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    <abstract>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 enhance models by incorporating both token-level and sentence-level translations, utilizing the extensive linguistic capabilities of modern LLMs, and incorporating available dictionary resources. 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.</abstract>
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%0 Conference Proceedings
%T Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing
%A Yang, Changbing
%A Nicolai, Garrett
%A Silfverberg, Miikka
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F yang-etal-2024-multiple
%X 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 enhance models by incorporating both token-level and sentence-level translations, utilizing the extensive linguistic capabilities of modern LLMs, and incorporating available dictionary resources. 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.
%R 10.18653/v1/2024.emnlp-main.261
%U https://aclanthology.org/2024.emnlp-main.261/
%U https://doi.org/10.18653/v1/2024.emnlp-main.261
%P 4537-4552

Markdown (Informal)

[Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing](https://aclanthology.org/2024.emnlp-main.261/) (Yang et al., EMNLP 2024)

• Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing (Yang et al., EMNLP 2024)

ACL

• Changbing Yang, Garrett Nicolai, and Miikka Silfverberg. 2024. Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4537–4552, Miami, Florida, USA. Association for Computational Linguistics.