比较两种文字统一方法在突厥语族中的跨语言迁移效果
Universal or Language-Family-Specific Script Unification for Cross-Lingual Transfer? A Case Study on Turkic Languages
- 用通用罗马化和突厥语专用文字统一方案处理11种突厥语
- 命名实体识别中两种方法表现相近，均显著优于单语基线
- 词性标注效果取决于语言特性和监督数据，无绝对优劣
密切相关的不同文字书写语言对多语言模型而言表面重叠极少，限制了跨语言迁移。本文比较了两种文字统一方法：通用的uroman罗马化与针对突厥语族的通用突厥文字（CTS）。我们在11种突厥语的音译维基百科语料上训练匹配的fastText模型，并在WikiANN命名实体识别和Universal Dependencies词性标注任务上评估。NER任务中，CTS与uroman表现无显著差异，两者均显著优于官方单语fastText基线。在POS任务中无统一优胜者：跨语言字符n-gram覆盖度由表示方式决定，而目标语言监督充足时，同语言覆盖度更为关键。尽管CANINE-c整体平均性能更高，但结构更简单的fastText系统在多个树库上仍具竞争力。总体而言，文字统一的有效性取决于语言、诱导的子词重叠度以及可用监督数据。
原文摘要 · Abstract (English)
Closely related languages written in different scripts expose little surface overlap to multilingual models, limiting cross-lingual transfer. We compare two approaches to script unification: the general-purpose uroman romanizer and the family-specific Common Turkic Script (CTS). We train matched fastText models on transliterated Wikipedia corpora from 11 Turkic languages and evaluate them on WikiANN named entity recognition and Universal Dependencies part-of-speech tagging. CTS and uroman show no significant difference on NER, while both substantially outperform the official monolingual fastText baselines. POS results reveal no universal winner: language-specific differences are associated with the cross-lingual character n-gram coverage induced by each representation, while within-language coverage becomes more important when target-language supervision is available. Although CANINE-c achieves higher overall POS averages, the substantially simpler fastText-based systems remain competitive on several treebanks. Overall, the effectiveness of script unification depends on the language, the induced subword overlap, and the available supervision.
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