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。