  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.
