为10种低资源语言构建了词义消歧语料，助力跨语言迁移研究。
SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods
- 采用半自动方法高效标注多义词语境数据
- 在10种低资源语言上验证了跨语言迁移效果
- 适合关注多语言NLP与公平评估的研究者
本文针对低资源语言缺乏高质量评测数据的问题，提出一种半自动标注方法，构建了涵盖十种低资源语言的多义词语境（WiC）标注数据集，覆盖多种语言家族和书写系统。通过在这些语言上开展基于WiC格式的实验，验证了数据集的有效性。结果表明，在低资源环境下，针对性的数据构建对多义词消歧和跨语言迁移至关重要。所发布的数据集与代码将支持更公平、稳健且真正的多语言自然语言处理研究。
原文摘要 · Abstract (English)
This paper addresses the critical need for high-quality evaluation datasets in low-resource languages to advance cross-lingual transfer. While cross-lingual transfer offers a key strategy for leveraging multilingual pretraining to expand language technologies to understudied and typologically diverse languages, its effectiveness is dependent on quality and suitable benchmarks. We release new sense-annotated datasets of sentences containing polysemous words, spanning ten low-resource languages across diverse language families and scripts. To facilitate dataset creation, the paper presents a demonstrably beneficial semi-automatic annotation method. The utility of the datasets is demonstrated through Word-in-Context (WiC) formatted experiments that evaluate transfer on these low-resource languages. Results highlight the importance of targeted dataset creation and evaluation for effective polysemy disambiguation in low-resource settings and transfer studies. The released datasets and code aim to support further research into fair, robust, and truly multilingual NLP.
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