Originating from China's Shang Dynasty approximately 3,000 years ago, the
Oracle Bone Script (OBS) is a cornerstone in the annals of linguistic history,
predating many established writing systems. Despite the discovery of thousands
of inscriptions, a vast expanse of OBS remains undeciphered, casting a veil of
mystery over this ancient language. The emergence of modern AI technologies
presents a novel frontier for OBS decipherment, challenging traditional NLP
methods that rely heavily on large textual corpora, a luxury not afforded by
historical languages. This paper introduces a novel approach by adopting image
generation techniques, specifically through the development of Oracle Bone
Script Decipher (OBSD). Utilizing a conditional diffusion-based strategy, OBSD
generates vital clues for decipherment, charting a new course for AI-assisted
analysis of ancient languages. To validate its efficacy, extensive experiments
were conducted on an oracle bone script dataset, with quantitative results
demonstrating the effectiveness of OBSD. Code and decipherment results will be
made available at this https URL

通过采用图像生成技术，本文引入了一种新颖的方法，即通过开发 Oracle Bone Script Decipher (OBSD)—— 一种基于条件扩散策略的图像生成方法，为 Oracle Bone Script（OBS）解密提供重要线索，为古代语言的人工智能辅助分析开辟了新的道路。

扩散模型解读甲骨文语言

Deciphering Oracle Bone Language with Diffusion Models

Handwriting recognition is a key technology for accessing the content of old
manuscripts, helping to preserve cultural heritage. Deep learning shows an
impressive performance in solving this task. However, to achieve its full
potential, it requires a large amount of labeled data, which is difficult to
obtain for ancient languages and scripts. Often, a trade-off has to be made
between ground truth quantity and quality, as is the case for the recently
introduced Bullinger database. It contains an impressive amount of over a
hundred thousand labeled text line images of mostly premodern German and Latin
texts that were obtained by automatically aligning existing page-level
transcriptions with text line images. However, the alignment process introduces
systematic errors, such as wrongly hyphenated words. In this paper, we
investigate the impact of such errors on training and evaluation and suggest
means to detect and correct typical alignment errors.

手写识别是访问古代手稿内容的关键技术，有助于保护文化遗产。深度学习在解决这一任务上表现出色，但要充分发挥其潜力，需要大量标记的数据，这在获取古代语言和文字方面是困难的。本文研究了对齐过程引入的系统性错误对训练和评估结果的影响，并提出了检测和纠正典型对齐错误的方法。

地面真实性对手写识别的影响

Impact of Ground Truth Quality on Handwriting Recognition

Constituency parsing plays a fundamental role in advancing natural language
processing (NLP) tasks. However, training an automatic syntactic analysis
system for ancient languages solely relying on annotated parse data is a
formidable task due to the inherent challenges in building treebanks for such
languages. It demands extensive linguistic expertise, leading to a scarcity of
available resources. To overcome this hurdle, cross-lingual transfer techniques
which require minimal or even no annotated data for low-resource target
languages offer a promising solution. In this study, we focus on building a
constituency parser for $\mathbf{M}$iddle $\mathbf{H}$igh $\mathbf{G}$erman
$\mathbf{MHG}$ under realistic conditions, where no annotated MHG treebank is
available for training. In our approach, we leverage the linguistic continuity
and structural similarity between MHG and $\mathbf{M}$odern $\mathbf{G}$erman
$\mathbf{MG}$, along with the abundance of MG treebank resources. Specifically,
by employing the $\mathit{delexicalization}$ method, we train a constituency
parser on MG parse datasets and perform cross-lingual transfer to MHG parsing.
Our delexicalized constituency parser demonstrates remarkable performance on
the MHG test set, achieving an F1-score of 67.3%. It outperforms the best
zero-shot cross-lingual baseline by a margin of 28.6% points. These encouraging
results underscore the practicality and potential for automatic syntactic
analysis in other ancient languages that face similar challenges as MHG.

本研究旨在通过使用去词法化方法，利用现代德语 (MG) 的语法分析数据，进行从现代德语到中古高地德语 (MHG) 的跨语言转移，训练了一个在 MHG 测试集上取得了 67.3% 的 F1 分数的词法成分分析器，相较于最佳零 - shot 跨语言基线提升了 28.6% 的性能。这些结果表明，在面临类似 MHG 的古代语言中进行自动句法分析具有实用性和潜力。