CORI：融合罗马化转写的CJKV基准——迈向超越文本脚本的跨语言迁移
CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts
AI总结：
本研究探讨源语言选择对跨语言迁移的影响，构建CJKV基准数据集，并提出融合罗马化转写与对比学习的方法，以增强跨语言表示并实现零样本迁移。
摘要：
天真地将英语视为源语言可能会忽视语言接触的重要性，从而阻碍许多语言的跨语言迁移。一些语言比其他语言联系更紧密，目标语言可以从与紧密相关语言的迁移中受益；对于许多语言而言，紧密相关语言集合并不包含英语。在本工作中，我们研究了源语言对跨语言迁移的影响，证明了选择与目标语言具有高接触度的源语言的重要性。我们还构建了一个针对紧密接触的汉日韩越（CJKV）语言的新型基准数据集，以进一步促进对语言接触的深入研究。为全面捕捉这些语言间的接触，我们提出通过对比学习目标整合超越文本脚本的罗马化转写，从而增强跨语言表示并实现有效的零样本跨语言迁移。
英文摘要：
Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact. Some languages are more well-connected than others, and target languages can benefit from transferring from closely related languages; for many languages, the set of closely related languages does not include English. In this work, we study the impact of source language for cross-lingual transfer, demonstrating the importance of selecting source languages that have high contact with the target language. We also construct a novel benchmark dataset for close contact Chinese-Japanese-Korean-Vietnamese (CJKV) languages to further encourage in-depth studies of language contact. To comprehensively capture contact between these languages, we propose to integrate Romanized transcription beyond textual scripts via Contrastive Learning objectives, leading to enhanced cross-lingual representations and effective zero-shot cross-lingual transfer.
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。
