未知文字：文字对跨语言迁移的影响
Unknown Script: Impact of Script on Cross-Lingual Transfer
AI总结：
本文研究源语言及文字对跨语言迁移的影响，通过多种分词方式预训练的单语与多语模型实验，证明分词器比共享文字、语言相似性和模型规模更关键。
摘要：
跨语言迁移已成为在不同语言之间迁移知识的有效方法。本文探讨该领域中一个常被忽视的方面：语言模型的源语言对语言迁移性能的影响。我们考虑目标语言及其文字均不属于预训练模型的情形。我们针对以不同分词方法预训练的单语和多语模型开展一系列实验，以确定影响向具有独特文字的新语言进行跨语言迁移的因素。研究结果揭示了分词器的重要性：相比共享文字、语言相似性和模型规模，它是更强的影响因素。
英文摘要：
Cross-lingual transfer has become an effective way of transferring knowledge between languages. In this paper, we explore an often overlooked aspect in this domain: the influence of the source language of a language model on language transfer performance. We consider a case where the target language and its script are not part of the pre-trained model. We conduct a series of experiments on monolingual and multilingual models that are pre-trained on different tokenization methods to determine factors that affect cross-lingual transfer to a new language with a unique script. Our findings reveal the importance of the tokenizer as a stronger factor than the shared script, language similarity, and model size.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
