Soft Language Prompts for Language Transfer

• URL: http://arxiv.org/abs/2407.02317v2
• Date: Wed, 30 Oct 2024 08:30:39 GMT
• Title: Soft Language Prompts for Language Transfer
• Authors: Ivan Vykopal, Simon Ostermann, Marián Šimko,
• Abstract summary: Cross-lingual knowledge transfer, especially between high- and low-resource languages, remains challenging in natural language processing (NLP) This study offers insights for improving cross-lingual NLP applications through the combination of parameter-efficient fine-tuning methods.
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• Abstract: Cross-lingual knowledge transfer, especially between high- and low-resource languages, remains challenging 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 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 16 languages, focusing on 10 mid- and low-resource languages. We further present to our knowledge the first use of soft prompts for language transfer, a technique we call 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 most configurations in many cases.

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