多语言性并无必要：探究感知任务中零样本迁移背后的因素
Multilinguality Does not Make Sense: Investigating Factors Behind Zero-Shot Transfer in Sense-Aware Tasks
AI总结：
本文通过28种语言的大规模分析，发现多语言性并非感知任务零样本迁移的必要条件，预训练与微调数据差异及评估工件更能解释其收益，并发布模型与基线以支持研究。
摘要：
跨语言迁移是现代自然语言处理的核心，使模型能够在与训练语言不同的语言上执行任务。一个常见的假设是，在更多语言上训练能提升零样本迁移性能。我们在感知任务（多义词和词汇语义变化）上对此进行测试，发现多语言性并非有效迁移的必要条件。我们跨28种语言的大规模分析表明，其他因素，如预训练和微调数据的差异以及评估工件，更能解释多语言性带来的表面收益。我们还发布了微调模型并提供了经验基线以支持未来研究。尽管聚焦于两个感知任务，我们的发现为跨语言迁移提供了更广泛的见解，尤其是对低资源语言而言。
英文摘要：
Cross-lingual transfer is central to modern NLP, enabling models to perform tasks in languages different from those they were trained on. A common assumption is that training on more languages improves zero-shot transfer. We test this on sense-aware tasks-polysemy and lexical semantic change-and find that multilinguality is not necessary for effective transfer. Our large-scale analysis across 28 languages reveals that other factors, such as differences in pretraining and fine-tuning data and evaluation artifacts, better explain the perceived benefits of multilinguality. We also release fine-tuned models and provide empirical baselines to support future research. While focused on two sense-aware tasks, our findings offer broader insights into cross-lingual transfer, especially for low-resource languages.
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。
