用卷积神经网络分析楔形文字泥板，发现作者有师生关系。
CNN-based Image Models Verify a Hypothesis that The Writers of Cuneiform Texts Improved Their Writing Skills When Studying at the Age of Hittite Empire
- 用CNN图像模型整体分析泥板，无需逐字分割
- 第一作者是教师，第二作者是练习写字的学生
- 为研究古代教育提供新方法，适合考古与数字人文学者
一块名为KBo 23.1 ++/KUB 30.38的楔形文字泥板，记录了基祖瓦特纳宗教仪式内容，由两位作者在两次迭代中书写了几乎相同的内容。不同于神话、文书等常见内容，这类重复泥板的留存原因尚不明确。为此，我们提出一种基于卷积神经网络（CNN）的图像分析新方法，无需逐个分割楔形符号即可定量分析泥板图像。结果表明，第一位书写者是‘教师’，第二位是正在练习书写技能的‘学生’。这一结论超越了传统语言学研究范畴。我们还讨论了该方法的相关推论及未来应用方向。
原文摘要 · Abstract (English)
A cuneiform tablet KBo 23.1 ++/KUB 30.38, which is known to represent a text of Kizzuwatna rituals, was written by two writers with almost identical content in two iterations. Unlike other cuneiform tablets that contained information such as myths, essays, or business records, the reason why ancient people left such tablets for posterity remains unclear. To study this problem, we develop a new methodology by analyzing images of a tablet quantitatively using CNN (Convolutional Neural Network)-based image models, without segmenting cuneiforms one-by-one. Our data-driven methodology implies that the writer writing the first half was a `teacher' and the other writer was a `student' who was training his skills of writing cuneiforms. This result has not been reached by classical linguistics. We also discuss related conclusions and possible further directions for applying our method and its generalizations.
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