Record linkage is a bedrock of quantitative social science, as analyses often
require linking data from multiple, noisy sources. Off-the-shelf string
matching methods are widely used, as they are straightforward and cheap to
implement and scale. Not all character substitutions are equally probable, and
for some settings there are widely used handcrafted lists denoting which string
substitutions are more likely, that improve the accuracy of string matching.
However, such lists do not exist for many settings, skewing research with
linked datasets towards a few high-resource contexts that are not
representative of the diversity of human societies. This study develops an
extensible way to measure character substitution costs for OCR'ed documents, by
employing large-scale self-supervised training of vision transformers (ViT)
with augmented digital fonts. For each language written with the CJK script, we
contrastively learn a metric space where different augmentations of the same
character are represented nearby. In this space, homoglyphic characters - those
with similar appearance such as ``O'' and ``0'' - have similar vector
representations. Using the cosine distance between characters' representations
as the substitution cost in an edit distance matching algorithm significantly
improves record linkage compared to other widely used string matching methods,
as OCR errors tend to be homoglyphic in nature. Homoglyphs can plausibly
capture character visual similarity across any script, including low-resource
settings. We illustrate this by creating homoglyph sets for 3,000 year old
ancient Chinese characters, which are highly pictorial. Fascinatingly, a ViT is
able to capture relationships in how different abstract concepts were
conceptualized by ancient societies, that have been noted in the archaeological
literature.

使用大量自我监督训练的视觉变换器（ViT），通过对数字化字体进行增强，开发了一种可扩展的方法来测量 OCR 文档的字符替换成本。使用字符表示之间的余弦距离作为编辑距离匹配算法中的替换成本，与其他广泛使用的字符串匹配方法相比，可以显著提高记录链接的精度，因为 OCR 错误往往是同形异义字的。 通过创建古代汉字的同形异义字集合，可以捕捉古代社会不同抽象概念的关系。

使用 Vision Transformers 量化字符相似度

Quantifying Character Similarity with Vision Transformers

Models for text-to-image synthesis, such as DALL-E~2 and Stable Diffusion,
have recently drawn a lot of interest from academia and the general public.
These models are capable of producing high-quality images that depict a variety
of concepts and styles when conditioned on textual descriptions. However, these
models adopt cultural characteristics associated with specific Unicode scripts
from their vast amount of training data, which may not be immediately apparent.
We show that by simply inserting single non-Latin characters in a textual
description, common models reflect cultural stereotypes and biases in their
generated images. We analyze this behavior both qualitatively and
quantitatively, and identify a model's text encoder as the root cause of the
phenomenon. Additionally, malicious users or service providers may try to
intentionally bias the image generation to create racist stereotypes by
replacing Latin characters with similarly-looking characters from non-Latin
scripts, so-called homoglyphs. To mitigate such unnoticed script attacks, we
propose a novel homoglyph unlearning method to fine-tune a text encoder, making
it robust against homoglyph manipulations.

通过在文本描述中插入单个非拉丁字符，我们展示了普通模型如何反映文化刻板印象和偏见。我们定性和定量分析了这种行为，并将其归因于模型的文本编码器。此外，我们提出了一种新颖的同形学习方法，通过微调文本编码器，使其能够抵抗同形符号的操纵.