用两阶段框架学习书写系统相似性，无需历史关系标签
Contrastive-to-Self-Supervised: A Two-Stage Framework for Script Similarity Learning
- 先用人工字母训练教师模型，再通过知识蒸馏迁移至真实古文字
- 在无标注情况下实现少样本字形识别与有意义的书写系统聚类
- 适合研究文字演化、跨语言对比的学者使用
学习字形与书写系统的相似性面临根本挑战：虽然人造字母中的单个字符可准确标注，但不同书写系统间的历史关联仍不确定且存在争议。我们提出一种两阶段框架，以应对这一认识论限制。首先，在标注的人造字母上使用对比损失训练编码器，建立具备强区分能力的教师模型；其次，通过教师-学生知识蒸馏，将教师的知识迁移至真实历史书写系统，学生在无监督下学习表示，同时自由发现潜在的跨系统相似性。非对称设置使学生能学习形变不变的嵌入，同时继承来自清晰样本的判别结构。该方法连接了有监督对比学习与无监督发现，既能区分不同系统间的明确边界，又能体现潜在历史影响带来的软相似性。在多种书写系统上的实验表明，该方法可在不依赖真实演化关系的前提下，实现有效的少样本字形识别和有意义的书写系统聚类。
原文摘要 · Abstract (English)
Learning similarity metrics for glyphs and writing systems faces a fundamental challenge: while individual graphemes within invented alphabets can be reliably labeled, the historical relationships between different scripts remain uncertain and contested. We propose a two-stage framework that addresses this epistemological constraint. First, we train an encoder with contrastive loss on labeled invented alphabets, establishing a teacher model with robust discriminative features. Second, we extend to historically attested scripts through teacher-student distillation, where the student learns unsupervised representations guided by the teacher's knowledge but free to discover latent cross-script similarities. The asymmetric setup enables the student to learn deformation-invariant embeddings while inheriting discriminative structure from clean examples. Our approach bridges supervised contrastive learning and unsupervised discovery, enabling both hard boundaries between distinct systems and soft similarities reflecting potential historical influences. Experiments on diverse writing systems demonstrate effective few-shot glyph recognition and meaningful script clustering without requiring ground-truth evolutionary relationships.
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