BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer

35Citations
Citations of this article
37Readers
Mendeley users who have this article in their library.

Abstract

Adapter modules enable modular and efficient zero-shot cross-lingual transfer, where current state-of-the-art adapter-based approaches learn specialized language adapters (LAs) for individual languages. In this work, we show that it is more effective to learn bilingual language pair adapters (BAs) when the goal is to optimize performance for a particular source-target transfer direction. Our novel BAD-X adapter framework trades off some modularity of dedicated LAs for improved transfer performance: we demonstrate consistent gains in three standard downstream tasks, and for the majority of evaluated low-resource languages.

Cite

CITATION STYLE

APA

Parović, M., Glavaš, G., Vulić, I., & Korhonen, A. (2022). BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer. In NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 1791–1799). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.naacl-main.130

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free