Cross-lingual knowledge transfer, especially between high- and low-resource languages, remains a challenge in natural language processing (NLP). This study offers insights for improving cross-lingual NLP applications through the combination of parameter-efficient fine-tuning methods. We systematically explore strategies for enhancing this cross-lingual transfer through the incorporation of language-specific and task-specific adapters and soft prompts. We present a detailed investigation of various combinations of these methods, exploring their efficiency across six languages, focusing on three low-resource languages, including the to our knowledge first use of soft language prompts. Our findings demonstrate that in contrast to claims of previous work, a combination of language and task adapters does not always work best; instead, combining a soft language prompt with a task adapter outperforms other configurations in many cases.

翻译：跨语言知识迁移，尤其是在高资源语言与低资源语言之间，仍然是自然语言处理（NLP）领域的一个挑战。本研究通过结合参数高效微调方法，为改进跨语言NLP应用提供了见解。我们系统地探索了通过融入语言特定和任务特定的适配器以及软提示来增强这种跨语言迁移的策略。我们对这些方法的各种组合进行了详细研究，探讨了其在六种语言上的效率，重点关注三种低资源语言，其中包括据我们所知首次使用的软语言提示。我们的研究结果表明，与先前工作的主张相反，语言适配器和任务适配器的组合并不总是效果最佳；在许多情况下，将软语言提示与任务适配器相结合的表现优于其他配置。