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    <abstract>State-of-the-art cross-lingual transfer often relies on massive multilingual models, but their prohibitive size and computational cost limit their practicality for low-resource languages. An alternative is to adapt powerful, task-specialized monolingual models, but this presents challenges in bridging the vocabulary and structural gaps between languages. To address this, we propose KDA, a Knowledge Distillation Adapter framework that efficiently adapts a fine-tuned, high-resource monolingual model to a low-resource target language. KDA utilizes knowledge distillation to transfer the source model’s task-solving capabilities to the target language in a parameter-efficient manner. In addition, we introduce a novel adapter architecture that integrates source-language token embeddings while learning new positional embeddings, directly mitigating cross-lingual representational mismatches. Our empirical results on zero-shot transfer for Vietnamese Sentiment Analysis demonstrate that KDA significantly outperforms existing methods, offering a new, effective, and computationally efficient pathway for cross-lingual transfer.</abstract>
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%0 Conference Proceedings
%T KDA: Knowledge Distillation Adapter for Cross-Lingual Transfer
%A Nguyen, Ta-Bao
%A Phan, Nguyen-Phuong
%A Le, Tung
%A Nguyen, Huy Tien
%Y Flek, Lucie
%Y Narayan, Shashi
%Y Phương, Lê Hồng
%Y Pei, Jiahuan
%S Proceedings of the 18th International Natural Language Generation Conference
%D 2025
%8 October
%I Association for Computational Linguistics
%C Hanoi, Vietnam
%F nguyen-etal-2025-kda
%X State-of-the-art cross-lingual transfer often relies on massive multilingual models, but their prohibitive size and computational cost limit their practicality for low-resource languages. An alternative is to adapt powerful, task-specialized monolingual models, but this presents challenges in bridging the vocabulary and structural gaps between languages. To address this, we propose KDA, a Knowledge Distillation Adapter framework that efficiently adapts a fine-tuned, high-resource monolingual model to a low-resource target language. KDA utilizes knowledge distillation to transfer the source model’s task-solving capabilities to the target language in a parameter-efficient manner. In addition, we introduce a novel adapter architecture that integrates source-language token embeddings while learning new positional embeddings, directly mitigating cross-lingual representational mismatches. Our empirical results on zero-shot transfer for Vietnamese Sentiment Analysis demonstrate that KDA significantly outperforms existing methods, offering a new, effective, and computationally efficient pathway for cross-lingual transfer.
%U https://aclanthology.org/2025.inlg-main.8/
%P 122-133

Markdown (Informal)

[KDA: Knowledge Distillation Adapter for Cross-Lingual Transfer](https://aclanthology.org/2025.inlg-main.8/) (Nguyen et al., INLG 2025)

• KDA: Knowledge Distillation Adapter for Cross-Lingual Transfer (Nguyen et al., INLG 2025)

ACL

• Ta-Bao Nguyen, Nguyen-Phuong Phan, Tung Le, and Huy Tien Nguyen. 2025. KDA: Knowledge Distillation Adapter for Cross-Lingual Transfer. In Proceedings of the 18th International Natural Language Generation Conference, pages 122–133, Hanoi, Vietnam. Association for Computational Linguistics.