无需标注数据，用自监督方法检测多语言词汇借词。
Self-Supervised Borrowing Detection on Multilingual Wordlists
- 结合全局对应模型与语音特征对比学习。
- 在基准数据集上性能媲美甚至超越有监督方法。
- 支持不同规模数据，适合无监督研究者使用。
本文提出一种完全自监督的多语言词汇借词检测方法。该方法融合两种信息源：基于全局对应模型的PMI相似度，以及在语音特征向量上训练的轻量级对比学习组件。此外，还设计了无需标签数据的自动决策阈值选择流程。在基准数据集上的实验表明，仅使用PMI即优于现有字符串相似度度量（如NED和SCA），而联合相似度表现与或优于有监督基线。消融实验揭示了字符编码、温度设置及增强策略的重要性。该方法可扩展至不同规模数据集，无需人工标注，并提供命令行工具供研究者自行开展实验。
原文摘要 · Abstract (English)
This paper presents a fully self-supervised approach to borrowing detection in multilingual wordlists. The method combines two sources of information: PMI similarities based on a global correspondence model and a lightweight contrastive component trained on phonetic feature vectors. It further includes an automatic procedure for selecting decision thresholds without requiring labeled data. Experiments on benchmark datasets show that PMI alone already improves over existing string similarity measures such as NED and SCA, and that the combined similarity performs on par with or better than supervised baselines. An ablation study highlights the importance of character encoding, temperature settings and augmentation strategies. The approach scales to datasets of different sizes, works without manual supervision and is provided with a command-line tool that allows researchers to conduct their own studies.
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